An underwater long linear array three-dimensional array shape accurate positioning and detection compensation method based on optical fiber sensing and seabed topographic profile matching

Abstract

The application provides an underwater long linear array three-dimensional array shape accurate positioning and detection compensation method based on optical fiber sensing and seabed topographic profile matching. The method continuously obtains strain, curvature, bending direction or spatial shape information of the array along the length direction by embedding an optical fiber sensing unit in the underwater long linear array cable body; obtains fine-grained three-dimensional topographic data of the array laying area in advance or synchronously, including seabed depth, topographic relief and corresponding geographical position; then performs profile matching and multi-source fusion on the array relative three-dimensional shape, array depth observation, endpoint position constraint and seabed topographic data obtained by the optical fiber sensing, and solves the absolute three-dimensional coordinates of each array element in the geographical coordinate system; finally, the array orientation vector and the propagation time delay model are reconstructed based on the real array element coordinates, which are used for conventional beam forming, adaptive beam forming, matched field processing, target bearing estimation and detection performance compensation.

Description

Technical Field

[0001] This invention relates to the fields of underwater acoustic detection, long linear array sonar, fiber optic sensing, underwater array formation estimation, underwater terrain matching and positioning, and array signal processing. In particular, it relates to a method for precise three-dimensional array positioning and detection compensation of underwater long linear arrays based on fiber optic sensing and seabed topographic profile matching. Specifically, it involves embedding fiber optic sensing units in an underwater long linear array to obtain the array's three-dimensional shape, and combining this with fine-grained three-dimensional seabed topographic data, depth data, and geographical location information of the array deployment area. Through profile matching and multi-source fusion calculation, the precise three-dimensional position of each array element is obtained, and then the three-dimensional positions of the array elements are used for beamforming, azimuth estimation, and target detection compensation. Background Technology

[0002] Underwater long-aperture arrays typically consist of multiple hydrophone elements deployed along a flexible cable. They rely on a large array aperture to achieve passive underwater target detection, azimuth estimation, weak signal detection, and underwater environmental monitoring. In theoretical design, long-aperture arrays are usually modeled as ideal linear arrays, uniform linear arrays, or predetermined curved arrays, with the distances between elements, array attitude, and spatial positions considered known parameters. However, in real underwater deployment environments, the array cable is affected by factors such as ocean currents, hydrodynamic loads, buoyancy elements, mooring constraints, seabed undulations, deployment errors, towing tension, or long-term sedimentation. The actual array shape often deviates from the ideal shape, exhibiting overall offset, bending, torsion, local arching, bottom contact, or lateral swaying. For long-aperture arrays, even small element position errors can cause phase compensation errors and time delay modeling errors, leading to beam main lobe shift, side lobe rise, null position inaccuracy, target azimuth estimation deviation, and decreased detection performance.

[0003] Existing methods for estimating the shape of underwater long linear arrays mainly include endpoint positioning, pressure sensor-assisted methods, attitude sensor inference methods, acoustic transponder positioning, hydrodynamic model inference methods, and array inversion methods based on target signals. Endpoint positioning can only constrain the positions of the array's ends, making it difficult to describe the continuous deformation in the middle of the array; pressure sensors can only provide local depth and cannot independently determine the horizontal position and three-dimensional curvature; attitude sensors suffer from drift and deployment density limitations; acoustic transponder positioning requires additional reference equipment, and its accuracy is easily affected in shallow, multipath, and noisy environments; hydrodynamic models are highly dependent on ocean currents, cable parameters, and boundary conditions; and acoustic signal-based inversion methods are easily affected by changes in target signals, sound velocity profiles, and the propagation environment. On the other hand, underwater mapping technology can acquire high-resolution three-dimensional seabed topographic data, but existing long linear array shape estimation methods typically do not deeply integrate the array's own deformation sensing results with the seabed topographic profile of the deployment area, making it difficult to accurately register the array's relative shape to the real geographic coordinate system.

[0004] Therefore, a new underwater long linear array three-dimensional array precise positioning technology is needed to enable the array not only to perceive its own continuous three-dimensional shape, but also to achieve absolute geographical location registration with the help of fine-grained three-dimensional seabed topographic data of the deployment area, and further use the real array element positions for acoustic array processing, thereby improving the detection accuracy, positioning stability and long-term working reliability of the long linear array detection system in complex seabed environments. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the prior art and to propose a method for precise positioning and detection compensation of an underwater long linear array three-dimensional array based on fiber optic sensing and matching of seabed topographic profiles.

[0006] This invention is achieved through the following technical solution: This invention proposes a method for precise positioning and detection compensation of an underwater long linear array three-dimensional formation based on fiber optic sensing and seabed topographic profile matching. The method includes: Step 1: Establish an underwater long linear array with fiber optic sensing capability; arrange the fiber optic sensing units along the axial direction of the long linear array cable inside the cable body, on the surface, or in the protective groove, and ensure that they are stably mechanically coupled to the cable body. Step 2: Preprocess the fiber optic sensing data and reconstruct the 3D array; Step 3: Acquire fine-grained 3D seabed topographic data of the array deployment area; Step 4: Establish a spatial transformation model between the array's relative coordinate system and the geographic coordinate system; regard the array's relative three-dimensional coordinates obtained by fiber optic sensing as a spatial curve in the array's own coordinate system, and regard the array's actual position in the seabed topography model as a spatial curve in the local geographic coordinate system. Step 5: Perform seabed topographic profile matching; the profile matching is to compare the similarity between the depth change profile measured or reconstructed by the array itself and the topographic depth profile of the candidate path in the seabed topographic database, and solve the optimal horizontal position, heading, attitude and height above the bottom of the array in the geographic coordinate system. Step 6: Perform multi-source fusion positioning to output the absolute three-dimensional position of the array elements; Step 7: Use the absolute three-dimensional position of the array elements for underwater acoustic detection compensation.

[0007] Furthermore, in step one, the fiber optic sensing unit employs one or more combinations of multi-core fiber, fiber optic grating array, distributed fiber optic strain sensor, optical frequency domain reflective shape sensor, or distributed acoustic sensing fiber. When multi-core fiber is used, the spatial positions of different cores relative to the cable cross-section are known, and the local bending direction and curvature of the cable are obtained by measuring the strain differences of different cores. When fiber optic grating array is used, multiple grating sensing points are distributed along the length direction, and the strain and temperature compensation results at the corresponding positions are calculated by grating wavelength drift. When distributed fiber optic strain sensor is used, the strain distribution, vibration distribution, or bending response over the entire length of the array is obtained by continuous sampling along the fiber.

[0008] Furthermore, in step two, after the fiber optic demodulation equipment acquires the fiber optic sensing signal, it first performs temperature compensation, zero-point calibration, noise filtering, outlier removal, and length coordinate unification processing; let the arc length of the array along the cable direction be... Fiber optic cable in arc length The strain vector measured at the location is The sensing mapping matrix obtained through pre-calibration Calculate the curvature vector at the current position. ,Right now:

[0009] After obtaining the curvature, bending direction, and attitude increments along the array direction, the array centerline is reconstructed using line integration, quaternion recursion, Frenet frame recursion, or discrete rigid body segment splicing. For discrete arrays, the long linear array is divided into multiple short segments, denoted as the first... The local pose matrix of each sampling point is: The relative position is The sampling interval is Then it can be deduced recursively as follows:

[0010]

[0011] in, Let represent the antisymmetric matrix composed of curvature vectors; through the above recursion, the relative three-dimensional centerline of the long linear array in its own coordinate system can be obtained:

[0012] Then, based on the installation arc length position of the acoustic array elements on the cable... The value of the insertion is up to the first The relative three-dimensional coordinates of each array element: .

[0013] Furthermore, the three-dimensional seabed topographic data includes seabed depth, topographic relief, and corresponding geographical location; the geographic coordinates are transformed to a local northeast-sky coordinate system or a local planar coordinate system to establish a digital depth model of the seabed.

[0014] in, and This represents the local horizontal coordinates of the current position. This indicates the current seabed depth.

[0015] Furthermore, in step four, the relationship is established by revising the process:

[0016] in, For the first The three-dimensional coordinates of each array element in the local geographic coordinate system Here is the attitude rotation matrix. It is a translation vector. These are correction terms corresponding to fiber reconfiguration errors, local elasticity errors of the cable body, or installation errors.

[0017] Furthermore, in step five, let the first... The measured depth of each sampling point or array element is The candidate horizontal coordinates are obtained by coordinate transformation. The corresponding seabed topography depth is The objective function for profile matching is constructed as follows:

[0018] in, For robust error functions, the Huber or Tukey function is used to reduce the impact of abnormal depth points, local buried points, or abrupt terrain changes. The shape consistency error between the reconstructed and optimized fiber array; For endpoint or anchor point position constraint errors; Error constrained by array space curve smoothness; , , These are the weighting coefficients.

[0019] Furthermore, the profile matching process employs a two-stage solution. The first stage is coarse matching, which establishes a candidate search space near the endpoint positions, deployment tracks, or preset deployment areas. Grid search or particle search is performed for different translational amounts, heading angles, pitch angles, and lateral offsets. The matching error between the terrain profile and the array measurement profile for each candidate path is calculated, and several candidate solutions with relatively small errors are selected. The second stage is fine matching, which uses the coarse matching candidate solutions as initial values ​​and employs optimization methods to jointly optimize the array's spatial attitude, horizontal position, depth deviation, height above the bottom, and local deformation correction to obtain the optimal three-dimensional position of the array.

[0020] Furthermore, in step six, the position states of all array elements are assumed to be:

[0021] The optimization objective can then be written as:

[0022] in, This represents the relative position, curvature, and shape consistency error of adjacent array elements obtained by fiber optic sensing. This indicates the depth error obtained from a pressure sensor or depth measurement. This indicates the matching error between the array depth profile and the seabed topography model; Indicates the geographic coordinate error of the endpoint, anchor point, or reference point; This represents the local attitude error measured by the attitude sensor; This indicates the continuity and smoothness error of the array curve; to Assign weights to each constraint term; after optimization, convert the array element positions in the local geographic coordinate system into longitude, latitude, and depth coordinates, and output the first... The absolute three-dimensional position of each array element:

[0023] in, Longitude Latitude For depth.

[0024] Furthermore, in step seven, the position of the receiving array element is determined. or local coordinates The array steering vector is recalculated based on the target's assumed azimuth, elevation angle, range, sound velocity profile, and propagation model; for far-field plane waves, the unit vector of the target's incident direction is assumed to be... The speed of sound in water is , No. The relative propagation delay of each array element is:

[0025] The corresponding narrowband steering vector is:

[0026] For near-field targets, let the candidate target position be... Then the first The propagation delay of each array element is expressed as: .

[0027] The present invention also proposes an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the underwater long linear array three-dimensional array precise positioning and detection compensation method based on fiber optic sensing and seabed topographic profile matching.

[0028] The beneficial effects of this invention are: This invention achieves a complete closed loop from "array relative shape perception" to "absolute registration with seabed topography" and then to "acoustic detection compensation." Compared with traditional processing methods that only use endpoint interpolation or ideal array shapes, this invention can continuously acquire the true three-dimensional shape of long linear arrays, improve the absolute positioning accuracy of the array by utilizing seabed topographic features, output the true three-dimensional coordinates and position uncertainty of each array element, and correct the guide vector mismatch caused by array distortion during subsequent acoustic processing. This improves the detection capability of underwater long linear arrays in shallow seas, complex seabed topography, long-period deployment, and strong multipath environments. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0030] Figure 1 This is a flowchart of a method for precise positioning and detection compensation of an underwater long linear array three-dimensional formation based on fiber optic sensing and seabed topographic profile matching. It is used to represent the complete process from fiber optic sensing data acquisition, relative formation reconstruction, three-dimensional topographic data acquisition, coordinate transformation, profile matching, multi-source fusion positioning to beamforming compensation.

[0031] Figure 2 This is a schematic diagram of the matching between a long linear array and seabed topographic profiles, used to illustrate the matching relationship between the depth or shape profiles measured by the array and the candidate path profiles in the seabed topographic database.

[0032] Figure 3This is a schematic diagram of the beamforming response before and after compensation of the three-dimensional real array based on simulation. It is used to show that after the absolute three-dimensional position of the array element is input into the acoustic processing module, the steering vector is reconstructed and the azimuth estimation, target detection and positioning compensation are completed. Detailed Implementation

[0033] 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.

[0034] This invention proposes a method for precise positioning and detection compensation of underwater long linear arrays in three dimensions based on fiber optic sensing and seabed topographic profile matching. The method involves embedding fiber optic sensing units within the underwater long linear array cable to continuously acquire information on the array's strain, curvature, bending direction, or spatial shape along its length. Fine-grained three-dimensional topographic data of the array deployment area, including seabed depth, topographic relief, and corresponding geographical location, is obtained through pre-measurement or synchronous measurement. The relative three-dimensional shape of the array, array depth observation, endpoint position constraints, and seabed topographic data obtained from fiber optic sensing are then profiled and fused using multi-source methods to calculate the absolute three-dimensional coordinates of each array element in the geographic coordinate system. Finally, the array steering vector and propagation delay model are reconstructed based on the actual element coordinates for use in conventional beamforming, adaptive beamforming, matched field processing, target azimuth estimation, and detection performance compensation.

[0035] The underwater long linear array body is a flexible or semi-flexible cable structure, with multiple acoustic array elements arranged along the cable direction. The fiber optic sensing units and the acoustic array elements have a fixed or calibrable spatial positional relationship. The fiber optic sensing units can be one or more combinations of multi-core optical fibers, fiber optic grating arrays, distributed fiber optic strain sensors, optical frequency domain reflective shape sensors, or distributed acoustic sensing fibers. Preferably, the fiber optic sensing units are continuously arranged along the array length, and a mapping relationship between the fiber optic measurement points and the acoustic array element numbers is established before the array leaves the factory or is deployed, so that the spatial curves sensed by the fiber optics can correspond to the relative position of each acoustic array element.

[0036] Specifically, in combination Figures 1-3 This invention proposes a method for precise positioning and detection compensation of an underwater long linear array three-dimensional formation based on fiber optic sensing and seabed topographic profile matching. The method specifically includes: Step 1: Establish an underwater long linear array with fiber optic sensing capabilities. Fiber optic sensing units are deployed along the axial direction of the long linear array cable, either inside the cable body, on its surface, or within a protective groove, ensuring stable mechanical coupling with the cable body. When using multi-core fibers, the spatial positions of different cores relative to the cable cross-section are known. The local bending direction and curvature of the cable body are obtained by measuring the strain differences between the different cores. When using a fiber grating array, multiple grating sensing points are distributed along the length direction. Strain and temperature compensation results at corresponding locations are calculated by shifting the grating wavelength. When using distributed fiber optic sensing, strain distribution, vibration distribution, or bending response over the entire array length is obtained through continuous sampling along the fiber. To improve the reliability of array shape estimation, pressure sensors, attitude sensors, or endpoint positioning modules can be installed at several key locations on the long linear array. Pressure sensors are used to obtain local water depth, attitude sensors are used to obtain local pitch, roll, or heading constraints, and endpoint positioning modules are used to provide the geographical coordinates of the array endpoints or mooring points.

[0037] Step Two: Preprocessing and 3D Array Reconstruction of Fiber Optic Sensing Data. After acquiring the fiber optic sensing signals, the fiber optic demodulation equipment first performs temperature compensation, zero-point calibration, noise filtering, outlier removal, and length coordinate unification. Let the arc length of the array along the cable direction be... Fiber optic cable in arc length The strain vector measured at the location is The sensing mapping matrix obtained through pre-calibration Calculate the curvature vector at that location. ,Right now:

[0038] After obtaining the curvature, bending direction, and attitude increments along the array direction, the array centerline is reconstructed using line integration, quaternion recursion, Frenet frame recursion, or discrete rigid body segment splicing. For discrete arrays, a long linear array can be divided into multiple short segments, let the first segment be... The local pose matrix of each sampling point is: The relative position is The sampling interval is Then it can be deduced recursively as follows:

[0039]

[0040] in, This represents the antisymmetric matrix composed of curvature vectors. Through the above recursion, the relative three-dimensional centerline of the long linear array in its own coordinate system can be obtained:

[0041] Then, based on the installation arc length position of the acoustic array elements on the cable... The value of the insertion is up to the first The relative three-dimensional coordinates of each array element:

[0042] Step 3: Acquire fine-grained 3D seabed topographic data of the array deployment area. This topographic data can be obtained using a multibeam echo sounder, side-scan sonar, underwater mapping platform, unmanned surface vessel mapping system, seabed topographic database, or the echo sounding equipment on the deployment vessel. The topographic data should at least include the longitude, latitude, and depth information of the topographic points, and may also include slope, roughness, topographic profile, measurement time, tidal correction value, and coordinate reference information. To facilitate profile matching, the geographic coordinates are first transformed to a local northeast-sky coordinate system or a local plane coordinate system to establish a seabed digital depth model.

[0043] in, and Represents local horizontal coordinates. This indicates the seabed depth at that location. For discrete topographic point cloud data, continuous topographic depth models can be constructed using triangular mesh interpolation, raster interpolation, bilinear interpolation, or Gaussian process interpolation. For topographic data acquired at different times, tidal level correction, depth benchmark unification, and coordinate benchmark unification are also required to ensure that the array pressure sensor depth, seabed bathymetry depth, and geographic location information are within the same reference frame.

[0044] Step 4: Establish a spatial transformation model between the array's relative coordinate system and the geographic coordinate system. The array's relative 3D coordinates obtained from fiber optic sensing are considered as spatial curves within the array's own coordinate system, while the array's actual position in the seabed topography model is considered as a spatial curve within a local geographic coordinate system. The relationship between the two is established through rotation, translation, and necessary local deformation corrections.

[0045] in, For the first The three-dimensional coordinates of each array element in the local geographic coordinate system Here is the attitude rotation matrix. It is a translation vector. This refers to correction terms corresponding to fiber optic reconfiguration errors, local elasticity errors in the cable, or installation errors. In practical applications, the endpoint positioning results, deployment tracks, or anchor point coordinates can be used to first provide... and The initial values ​​are then refined using profile matching and multi-source optimization.

[0046] Step 5: Perform seabed topographic profile matching. Profile matching compares the similarity of the depth variation profile measured or reconstructed by the array itself with the topographic depth profiles of candidate paths in the seabed topographic database to determine the optimal horizontal position, heading, attitude, and necessary height above the seabed for the array in the geographic coordinate system. Let the first step be... The measured depth of each sampling point or array element is The candidate horizontal coordinates are obtained by coordinate transformation. The corresponding seabed topography depth is If the array is deployed close to the seabed, the difference between the array depth and the seabed depth will be small. Profile matching can be constructed using the following objective function:

[0047] in, For robust error functions, the Huber or Tukey function is used to reduce the impact of abnormal depth points, local buried points, or abrupt terrain changes. The shape consistency error between the reconstructed and optimized fiber array; For endpoint or anchor point position constraint errors; Error constrained by array space curve smoothness; , , These are the weighting coefficients.

[0048] The profile matching process employs a two-stage solution. The first stage is coarse matching, which establishes a candidate search space near the endpoint positions, deployment tracks, or pre-defined deployment areas. Grid search or particle search is performed for different translational, heading, pitch, and lateral offsets to calculate the matching error between the terrain profile and the array measurement profile for each candidate path. Several candidate solutions with smaller errors are selected. The second stage is fine matching, using the coarse matching candidate solutions as initial values. Optimization methods such as nonlinear least squares or sliding window are employed to jointly optimize the array's spatial attitude, horizontal position, depth deviation, ground clearance, and local deformation correction to obtain the optimal 3D array position. For areas with distinct terrain features, profile similarity can be primarily relied upon; for relatively flat terrain or areas with multiple solutions, the weights of endpoint positioning, deployment tracks, attitude sensors, and array smoothing constraints can be increased.

[0049] Step Six: Perform multi-source fusion positioning to output the absolute 3D position of the array elements. The constraints of fiber shape, pressure depth, seabed topography profile matching, endpoint geographic coordinates, attitude, deployment trajectory, and array continuity are unified into the optimization framework. Assume the position states of all array elements are as follows:

[0050] The optimization objective can then be written as:

[0051] in, This represents the relative position, curvature, and shape consistency error of adjacent array elements obtained by fiber optic sensing. This indicates the depth error obtained from a pressure sensor or depth measurement. This indicates the matching error between the array depth profile and the seabed topography model; Indicates the geographic coordinate error of the endpoint, anchor point, or reference point; This represents the local attitude error measured by the attitude sensor; This indicates the continuity and smoothness error of the array curve; to The weights for each constraint term are defined. After optimization, the element positions in the local geographic coordinate system are converted into longitude, latitude, and depth coordinates, and the result is the output of the first... The absolute three-dimensional position of each array element:

[0052] in, Longitude Latitude For depth. To facilitate subsequent acoustic processing, this invention also outputs the position uncertainty matrix for each array element. This is used to describe the confidence level of the element position estimation. Position uncertainty can be obtained from the filter covariance, the optimized Hessian matrix approximation, the particle distribution variance, or the statistical dispersion of the multi-candidate matching results.

[0053] Step 7: Use the absolute three-dimensional position of the array elements for underwater acoustic detection compensation. Receiver element position. or local coordinates The array steering vector is recalculated based on the target's assumed azimuth, elevation angle, range, sound velocity profile, and propagation model. For far-field plane waves, the unit vector of the target incident direction is assumed to be... The speed of sound in water is , No. The relative propagation delay of each array element is:

[0054] The corresponding narrowband steering vector is:

[0055] For near-field targets, let the candidate target position be... Then the first The propagation delay of each array element can be expressed as:

[0056] When considering the effects of sound velocity profiles and seabed reflections, ray acoustics, normal modes, parabolic equations, or matched field propagation models can be used to calculate the propagation phase of each array element. The steering vector based on the actual array configuration can be used for conventional beamforming, adaptive beamforming, MVDR beamforming, MUSIC azimuth estimation, matched field processing, and multi-target detection. If there is uncertainty in the element positions, it can be determined according to... Construct a robust guide vector set or diagonally load the covariance matrix to reduce the impact of element position errors on adaptive processing.

[0057] Example The technical solution of the present invention will be further described below with reference to specific embodiments and simulation test results. The following embodiments are used to illustrate the present invention and not to limit the scope of protection of the present invention; without departing from the core idea of ​​the present invention, parameters such as the number of array elements, the spacing between array elements, the array length, the seabed topography model, the search step size, the weighting coefficient, the sound speed, the signal frequency, the target azimuth and the elevation angle can be adjusted according to the actual system.

[0058] In one specific embodiment, a simulation system for precise positioning and detection compensation of an underwater long linear array three-dimensional formation is constructed. This simulation system includes an underwater long linear array model, a fiber optic sensing reconstruction model, a pressure depth measurement model, a seabed digital depth model, a profile matching positioning model, a multi-source fusion positioning model, and an acoustic beamforming compensation model. The underwater long linear array comprises 64 acoustic array elements, with a nominal spacing of 8m between adjacent elements, and a total array length of 504m. To simulate actual underwater deployment, the long linear array does not adopt an ideal straight-line formation; instead, continuous curvature, local oscillations, and small-scale array perturbations are introduced in the horizontal plane to form a realistic three-dimensional formation that deviates from an ideal straight line.

[0059] In this embodiment, the actual horizontal translation of the array is set to 38.00m, and the actual height of the array above the seabed is set to 5.00m. The seabed digital depth model is constructed using a two-dimensional topographic function that includes gradually varying slopes, periodic undulations, local trenches, and local protrusions to simulate complex seabed topography in shallow waters. The seabed digital depth model can be considered as fine-grained three-dimensional seabed topographic data pre-obtained from multibeam echo sounding, side-scan sonar, or a seabed topography database. The actual depth of the array is obtained by subtracting the height above the seabed from the depth at the corresponding horizontal position, and a small amount of cable undulation is superimposed to simulate the actual spatial state of the array when it is deployed near the seabed.

[0060] In fiber optic sensing simulation, the system uses the relative three-dimensional shape of the real array as a basis, adding small random errors to simulate the reconstruction errors generated by multi-core optical fibers, fiber Bragg grating arrays, or distributed fiber optic sensors in actual measurements. Based on fiber optic sensing data, the system obtains the relative three-dimensional shape of the array along the arc length direction and outputs the relative coordinates of each array element in the array's own coordinate system. Simultaneously, pressure sensor depth observations are simulated at several or all array element locations, and random measurement noise is added to the actual depth to form a measured depth profile of the array.

[0061] Subsequently, candidate deployment paths are generated based on endpoint locations or deployment tracks. In this embodiment, the candidate horizontal translation is set within the range of 20.0m to 55.0m, and the candidate bottom clearance is set within the range of 1.0m to 9.0m. For each set of candidate horizontal translation and candidate bottom clearance, the array relative coordinates obtained from fiber reconstruction are transformed into a local geographic coordinate system, and the seabed depth on the corresponding candidate path is read from the seabed digital depth model to form a candidate topographic depth profile. The root mean square error between the array's measured depth profile and the candidate topographic depth profile is further calculated as the profile matching cost for that candidate path.

[0062] Through the coarse matching search described above, the system obtains the matching cost surface. Simulation results show that the matching cost surface forms a significant low-cost region near the actual parameters, indicating that the measured depth profile of the array and the candidate path profiles in the seabed topography database are matchable. In one simulation of this embodiment, the system estimated the horizontal translation to be 38.20m, which is close to the actual horizontal translation of 38.00m; the estimated height above the bottom was 4.94m, which is close to the actual height above the bottom of 5.00m; and the minimum root mean square error of the profile matching was 0.540m. These results demonstrate that seabed topography profile matching can effectively register the relative shape of the array obtained by fiber optic sensing to a local geographic coordinate system.

[0063] In the fine matching and multi-source fusion positioning stage, the candidate results obtained from coarse matching are used as initial values. Fiber shape constraints, pressure depth constraints, seabed topography profile matching constraints, endpoint position constraints, and array continuity constraints are uniformly incorporated into the optimization framework. Optimization variables include the array's horizontal position, height above the seabed, local deformation correction, and the three-dimensional position of each array element. After optimization, the system outputs the absolute three-dimensional position of each array element and can further output the position uncertainty for subsequent robust beamforming or array status assessment.

[0064] To verify the positioning effect of the method of the present invention, this embodiment sets up three comparison methods. The first is the ideal linear array or endpoint interpolation method. This method only interpolates the position of the middle array element based on the position of the array endpoints and cannot describe the continuous bending and local deformation in the middle of the array. The second is the fiber-only endpoint constraint method. This method uses the relative array shape and endpoint reference position obtained by fiber reconstruction, but does not use the seabed topographic profile for absolute position registration. Therefore, when there are endpoint errors, heading errors, scale errors, or local shape drifts, the absolute position of the array may still be deviated. The third is the method of the present invention, which integrates the fiber relative array shape, pressure depth observation, and seabed topographic profile matching results to output the absolute three-dimensional position of the array element.

[0065] Simulation results show that the ideal linear array or endpoint interpolation method suffers from significant 3D position errors of array elements due to neglecting the actual curvature of the array. In one simulation, the average 3D position error of the array elements using this method was 16.83 m, and the 95% error was 38.53 m. The fiber-optic endpoint constraint method alone can reflect the relative curvature of the array, but due to the lack of constraints on absolute position from the seabed topographic profile, overall attitude, scale, and local deformation errors still exist. In one simulation, the average 3D position error of the array elements using this method was 5.50 m, and the 95% error was 5.92 m. The method of this invention, through topographic profile matching and multi-source fusion positioning, reduces the average 3D position error of the array elements to 0.54 m and the 95% error to 0.96 m, significantly outperforming the two comparative methods mentioned above.

[0066] In the acoustic detection compensation simulation, the true azimuth angle of the far-field target was set to 28.0°, the target elevation angle to 8.0°, the sound speed in water to 1500 m / s, and the narrowband signal frequency to 120 Hz. A three-dimensional steering vector was constructed using the ideal linear array position, the fiber-only endpoint constraint position, the position estimated by the method of this invention, and the true array element position, and azimuth scanning was performed. For the ideal linear array compensation, due to the significant mismatch between the array element positions and the true array shape, the beamforming response exhibited main lobe shift and abnormal side lobe structure. For the fiber-only endpoint constraint compensation, because it can partially reflect the array curvature, the azimuth response was improved compared to the ideal linear array, but it still differed from the true array compensation result when heading errors, scale errors, local shape drift, and depth errors were present. For the method of this invention, because the absolute three-dimensional position of the array elements was corrected through terrain profile matching and multi-source fusion, the constructed steering vector was closer to the true array steering vector, and its beam peak could stably appear near the true target azimuth. Figure 3 The beamforming response diagrams before and after actual array compensation are used to represent the improvement effect on target azimuth estimation and detection compensation performance after the absolute three-dimensional position of the array elements is input into the acoustic processing module.

[0067] As can be seen from the above simulation examples, this invention does not rely solely on fiber optic sensing, nor solely on pressure depth or endpoint position. Instead, it integrates the continuous relative three-dimensional array shape obtained from fiber optic sensing, the seabed digital depth model, depth observation data, and endpoint constraints. It utilizes seabed topographic profile matching to achieve precise registration of the array's relative shape to the real geographic coordinate system, and further uses the estimated absolute three-dimensional positions of the array elements for acoustic beamforming and detection compensation. This technical solution effectively reduces the element position errors caused by traditional ideal array shapes or endpoint interpolation methods, minimizes steering vector mismatch, and improves the positioning accuracy, azimuth estimation stability, and detection reliability of long linear arrays under complex seabed topography and long-period deployment conditions. It possesses strong engineering applicability and patent protection value.

[0068] The present invention also proposes an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the underwater long linear array three-dimensional array precise positioning and detection compensation method based on fiber optic sensing and seabed topographic profile matching.

[0069] The memory in this application embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the methods described in this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0070] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0071] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0072] It should be noted that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied as execution by a hardware decoding processor, or as a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0073] The above provides a detailed description of the underwater long linear array three-dimensional array precise positioning and detection compensation method based on fiber optic sensing and seabed topographic profile matching proposed in this invention. Specific examples are used to illustrate the principle and implementation of this invention. The above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for precise positioning and detection compensation of an underwater long linear array three-dimensional formation based on fiber optic sensing and seabed topographic profile matching, characterized in that... The method includes: Step 1: Establish an underwater long linear array with fiber optic sensing capability; arrange the fiber optic sensing units along the axial direction of the long linear array cable inside the cable body, on the surface, or in the protective groove, and ensure that they are stably mechanically coupled to the cable body. Step 2: Preprocess the fiber optic sensing data and reconstruct the 3D array; Step 3: Acquire fine-grained 3D seabed topographic data of the array deployment area; Step 4: Establish a spatial transformation model between the array's relative coordinate system and the geographic coordinate system; regard the array's relative three-dimensional coordinates obtained by fiber optic sensing as a spatial curve in the array's own coordinate system, and regard the array's actual position in the seabed topography model as a spatial curve in the local geographic coordinate system. Step 5: Perform seabed topographic profile matching; the profile matching is to compare the similarity between the depth change profile measured or reconstructed by the array itself and the topographic depth profile of the candidate path in the seabed topographic database, and solve the optimal horizontal position, heading, attitude and height above the bottom of the array in the geographic coordinate system. Step 6: Perform multi-source fusion positioning to output the absolute three-dimensional position of the array elements; Step 7: Use the absolute three-dimensional position of the array elements for underwater acoustic detection compensation.

2. The method according to claim 1, characterized in that, In step one, the fiber optic sensing unit employs one or more combinations of multi-core fiber, fiber optic grating array, distributed fiber optic strain sensor, optical frequency domain reflective shape sensor, or distributed acoustic sensing fiber. When multi-core fiber is used, the spatial positions of different cores relative to the cable cross-section are known, and the local bending direction and curvature of the cable are obtained by measuring the strain differences of different cores. When fiber optic grating array is used, multiple grating sensing points are distributed along the length direction, and the strain and temperature compensation results at the corresponding positions are calculated by grating wavelength drift. When distributed fiber optic strain sensor is used, the strain distribution, vibration distribution, or bending response over the entire length of the array is obtained by continuous sampling along the fiber.

3. The method according to claim 1, characterized in that, In step two, after the fiber optic demodulation equipment acquires the fiber optic sensing signal, it first performs temperature compensation, zero-point calibration, noise filtering, outlier removal, and length coordinate unification. Let the arc length of the array along the cable direction be... Fiber optic cable in arc length The strain vector measured at the location is The sensing mapping matrix obtained through pre-calibration Calculate the curvature vector at the current position. ,Right now: After obtaining the curvature, bending direction, and attitude increments along the array direction, the array centerline is reconstructed using line integration, quaternion recursion, Frenet frame recursion, or discrete rigid body segment splicing. For discrete arrays, the long linear array is divided into multiple short segments, denoted as the first... The local pose matrix of each sampling point is: The relative position is The sampling interval is Then it can be deduced recursively as follows: in, Let represent the antisymmetric matrix composed of curvature vectors; through the above recursion, the relative three-dimensional centerline of the long linear array in its own coordinate system can be obtained: Then, based on the installation arc length position of the acoustic array elements on the cable... The value of the insertion is up to the first The relative three-dimensional coordinates of each array element: 。 4. The method according to claim 1, characterized in that, Three-dimensional seabed topographic data includes seabed depth, topographic relief, and corresponding geographical location; the geographic coordinates are transformed to a local northeast-sky coordinate system or a local planar coordinate system to establish a digital depth model of the seabed. in, and This represents the local horizontal coordinates of the current position. This indicates the current seabed depth.

5. The method according to claim 1, characterized in that, In step four, the relationship is established and modified: in, For the first The three-dimensional coordinates of each array element in the local geographic coordinate system Here is the attitude rotation matrix. It is a translation vector. These are correction terms corresponding to fiber reconfiguration errors, local elasticity errors of the cable body, or installation errors.

6. The method according to claim 1, characterized in that, In step five, let the first... The measured depth of each sampling point or array element is The candidate horizontal coordinates are obtained by coordinate transformation. The corresponding seabed topography depth is The objective function for profile matching is constructed as follows: in, For robust error functions, the Huber or Tukey function is used to reduce the impact of abnormal depth points, local buried points, or abrupt terrain changes. The shape consistency error between the reconstructed and optimized fiber array; For endpoint or anchor point position constraint errors; Error constrained by array space curve smoothness; , , These are the weighting coefficients.

7. The method according to claim 6, characterized in that, The profile matching process employs a two-stage solution. The first stage is coarse matching, which establishes a candidate search space near the endpoints, deployment tracks, or preset deployment areas. Grid search or particle search is performed for different translational amounts, heading angles, pitch angles, and lateral offsets. The matching error between the terrain profile and the array measurement profile for each candidate path is calculated, and several candidate solutions with relatively small errors are selected. The second stage is fine matching, which uses the coarse matching candidate solutions as initial values ​​and employs optimization methods to jointly optimize the array's spatial attitude, horizontal position, depth deviation, ground clearance, and local deformation correction to obtain the optimal three-dimensional position of the array.

8. The method according to claim 1, characterized in that, In step six, the position states of all array elements are assumed to be: The optimization objective can then be written as: in, This represents the relative position, curvature, and shape consistency error of adjacent array elements obtained by fiber optic sensing. This indicates the depth error obtained from a pressure sensor or depth measurement. This indicates the matching error between the array depth profile and the seabed topography model; Indicates the geographic coordinate error of the endpoint, anchor point, or reference point; This represents the local attitude error measured by the attitude sensor; This indicates the continuity and smoothness error of the array curve; to Assign weights to each constraint term; after optimization, convert the array element positions in the local geographic coordinate system into longitude, latitude, and depth coordinates, and output the first... The absolute three-dimensional position of each array element: in, Longitude Latitude For depth.

9. The method according to claim 1, characterized in that, In step seven, the position of the receiving array element or local coordinates The array steering vector is recalculated based on the target's assumed azimuth, elevation angle, range, sound velocity profile, and propagation model; for far-field plane waves, the unit vector of the target's incident direction is assumed to be... The speed of sound in water is , No. The relative propagation delay of each array element is: The corresponding narrowband steering vector is: For near-field targets, let the candidate target position be... Then the first The propagation delay of each array element is expressed as: 。 10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-9.

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