Underwater terrain contour matching and positioning method, device and equipment and storage medium

Abstract

According to the underwater terrain contour matching and positioning method, device and equipment and the storage medium, Zernike moments under respective polar coordinate systems are calculated based on a sounding point data set and a matching point data set, respective feature vector sets are constructed, matching point data with the highest similarity with the sounding point data set are selected from the matching point data set to serve as an optimal matching and positioning template, and the matching and positioning accuracy of the underwater terrain contour is improved. Accurate positioning is ensured; correcting the course angle deviation by using the phase angle information of the optimal matching positioning template feature vector, accurately adjusting the position of each sounding point, and interpolating and optimizing the inertial navigation indication track; a group of parallel and symmetrical reference sounding tracks are generated, the interval is dynamically adjusted by adopting a cross correlation algorithm so as to maximize the difference degree between terrain contours corresponding to the tracks, a highly optimized track terrain contour line set is obtained, the track terrain contour line set is finely matched with a prior digital map, and the precision of the track terrain contour line set is improved. And the central track of the optimal track group is selected as a position estimation basis, so that the influence of course indication deviation on a positioning result is effectively solved.

Description

Technical Field

[0001] This application relates to the field of underwater terrain-aided navigation, and particularly to an underwater terrain contour matching and positioning method, device, equipment, and storage medium. Background Art

[0002] A multibeam sonar detects the underwater terrain by transmitting sound waves and receiving their echoes. These sound wave data are used to generate a high-precision underwater topographic map. The underwater terrain matching and positioning method is to match these measured terrain data with a pre-stored high-resolution prior digital map, so as to estimate the current position of an AUV (Autonomous Underwater Vehicle) or other underwater vehicles.

[0003] In related technologies, the traditional multibeam underwater terrain matching and positioning method has a large amount of calculation, and the method of reducing the single-batch processing data with the terrain contour line as the matching mechanism will lose some depth information, resulting in the reduction of the convergence speed and matching accuracy of the matching and positioning results. Even if multiple parallel terrain contours are used to supplement the terrain information, it is still impossible to effectively solve the inertial navigation direction indication deviation, especially the influence of the initial deviation on the positioning result. Summary of the Invention

[0004] This application provides an underwater terrain contour matching and positioning method, device, equipment, and storage medium, aiming at the technical problems that the traditional multibeam underwater terrain matching and positioning method has a large amount of calculation, and the method of reducing the single-batch processing data with the terrain contour line as the matching mechanism will lose some depth information, resulting in the reduction of the convergence speed and matching accuracy of the matching and positioning results. Even if multiple parallel terrain contours are used to supplement the terrain information, it is still impossible to effectively solve the inertial navigation direction indication deviation, especially the influence of the initial deviation on the positioning result.

[0005] In a first aspect, an embodiment of this application provides a multibeam sonar underwater terrain contour matching and positioning method, and the multibeam sonar underwater terrain contour matching and positioning method includes:

[0006] Based on the sounding point data set and the matching point data set in the polar coordinate system, calculate the Zernike moments of different combinations, construct their respective feature vector sets, and use a metric index to evaluate the similarity, and select the matching point data with the highest similarity to the sounding point data set as the optimal matching and positioning template;

[0007] Based on the feature vectors of the sounding point data set and the optimal matching and positioning template, retain the set of phase angles corresponding to the key feature vectors, calculate the mean value of the angle difference to correct the heading angle deviation, and then adjust the positions of each sounding point and interpolate to obtain the corrected inertial navigation indicated track;

[0008] According to the corrected inertial navigation indicated track, generate a set of parallel and symmetrically distributed reference sounding tracks, keep an equal distance from the central track, and evaluate the similarity between the terrain profiles corresponding to the tracks through the cross-correlation algorithm. Adjust the distance according to the similarity feedback to optimize the tracks, and finally form an optimized track set;

[0009] Perform a matching operation on the measured terrain profile line corresponding to the track set and the terrain profile corresponding to the array of reference matching tracks on the prior digital map, and use the central track of the track group with the optimal evaluation index as the estimation basis for each position point in the current period.

[0010] Combined with the first aspect, in an implementation, before calculating the Zernike moments of different combinations of the two sets in the polar coordinate system in the sounding point data set and the matching point data set in the polar coordinate system, constructing their respective feature vector sets, and using a metric index to evaluate the similarity, and selecting the matching point data with the highest similarity to the sounding point data set as the optimal matching positioning template, it further includes:

[0011] Based on the effective measurement data set of the multi-beam sounding sonar at the sounding points, interpolate the effective measurement data set using the bilinear interpolation method to obtain a sounding data set in the form of grid nodes, perform normalization processing and reference point transformation on the sounding data set in the form of grid nodes to obtain a sounding data set symmetric about the grid center, perform coordinate transformation on the sounding data set symmetric about the grid center, convert the Cartesian coordinate system to the polar coordinate system, and take the data set within the unit circle to obtain the corresponding sounding point polar coordinate data set.

[0012] Combined with the first aspect, in an implementation, before calculating the Zernike moments of different combinations, constructing their respective feature vector sets, and using a metric index to evaluate the similarity in the sounding point data set and the matching point data set in the polar coordinate system, and selecting the matching point data with the highest similarity to the sounding point data set as the optimal matching positioning template, it further includes:

[0013] Determine a search sliding window based on the coordinates of the sounding data set symmetric about the grid center. Using the search sliding window as a template and the resolution of the grid cells of the prior digital map as the step size, slide within the matching search area of the sounding points to obtain a set of matching templates. Perform normalization, reference point transformation, and coordinate transformation on each matching template in the set of matching templates to obtain a matching point data set in the polar coordinate system.

[0014] Combined with the first aspect, in an implementation, after performing a matching operation on the measured terrain profile line corresponding to the track set and the terrain profile line corresponding to the array of reference matching tracks on the prior digital map, and using the central track of the track group with the optimal evaluation index as the estimation basis for each position point in the current period, it further includes:

[0015] The variance P of the inertial navigation position estimate corrected at time t t , the threshold percentage η of the decrease in the variance of the inertial navigation position estimate 下 , the threshold percentage η of the increase in the variance of the inertial navigation position estimate 上 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching are used to control the number of multi-beam sonar beam formations.

[0016] Combined with the first aspect, in an implementation, the variance P of the inertial navigation position estimate corrected at time t t , the threshold percentage η of the decrease in the variance of the inertial navigation position estimate 下 , the threshold percentage η of the increase in the variance of the inertial navigation position estimate 上 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching are used to control the number of multi-beam sonar beam formations, including:

[0017] If the inertial navigation position variance P at time t t , the threshold percentage η of the decrease in the variance of the inertial navigation position estimate 下 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching satisfy the formula P t ≤η 下 P0, then reduce the number of multi-beam sonar beam formations.

[0018] Combined with the first aspect, in an implementation, the variance P of the inertial navigation position estimate corrected at time t t , the threshold percentage η of the decrease in the variance of the inertial navigation position estimate 下 , the threshold percentage η of the increase in the variance of the inertial navigation position estimate 上 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching are used to control the number of multi-beam sonar beam formations, including:

[0019] If the inertial navigation position variance P at time t t , the threshold percentage η of the increase in the variance of the inertial navigation position estimate 上 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching satisfy the formula P t >η 上 P0, then increase the number of multi-beam sonar beam formations.

[0020] Combined with the first aspect, in an implementation, the variance P of the inertial navigation position estimate corrected at time t t , the threshold percentage η of the decrease in the variance of the inertial navigation position estimate 下 , the threshold percentage η of the increase in the variance of the inertial navigation position estimate 上 and the variance P0 of the inertial navigation indicated position at the initial time of terrain matching are used to control the number of multi-beam sonar beam formations, including:

[0021] If the threshold percentage η of the decrease in the variance of the inertial navigation position estimate下 、Percentage η of the rising threshold of the inertial navigation position estimation variance 上 Satisfies the formula η 下 P0 > P t > η 上 P0 and η 上 ≥ 2η 下 , then keep the number of multi-beam sonar beam formations unchanged.

[0022] In a second aspect, an embodiment of the present application provides a multi-beam sonar terrain contour matching and positioning device, and the multi-beam sonar terrain contour matching and positioning device includes:

[0023] A feature vector construction and terrain matching module, which is used to construct respective feature vector sets by calculating Zernike moments corresponding to different combinations based on the sounding point data set and the matching point data set in the polar coordinate system, and use a metric index to evaluate the similarity, and select the matching point data with the highest similarity to the sounding point data set as the optimal matching and positioning template;

[0024] A heading angle deviation correction and track adjustment module, which is used to retain the set of phase angles corresponding to the key feature vectors based on the feature vectors of the sounding point data set in the polar coordinate system and the optimal matching and positioning template, calculate the average value of the angle difference to correct the heading angle deviation, and then adjust the sounding point position and interpolate to obtain the corrected inertial navigation indicated track;

[0025] A track generation and optimization module, which is used to generate a set of reference sounding tracks that are parallel and centrally symmetrically distributed according to the corrected inertial navigation indicated track, keep an equal distance from the central track and evaluate the similarity between the tracks corresponding to the terrain contours through the cross-correlation algorithm, and adjust the distance according to the similarity feedback to optimize the track, and finally form an optimized track set;

[0026] A terrain contour matching and final positioning module, which is used to perform a matching operation on the measured terrain contour line corresponding to the track set and the terrain contour line corresponding to the array of reference matching tracks on the prior digital map, and use the central track of the track group with the optimal evaluation index as the estimation basis for each position point in the current period.

[0027] In a third aspect, an embodiment of the present application provides a multi-beam sonar terrain contour matching and positioning device, and the multi-beam sonar terrain contour matching and positioning device includes a processor, a memory, and a multi-beam sonar terrain contour matching and positioning program stored on the memory and executable by the processor. When the multi-beam sonar terrain contour matching and positioning program is executed by the processor, the steps of the multi-beam sonar underwater terrain contour matching and positioning method described in some of the above embodiments are implemented.

[0028] Fourthly, an embodiment of the present application provides a computer-readable storage medium, on which a multi-beam sonar terrain profile matching and positioning program is stored. When the multi-beam sonar terrain profile matching and positioning program is executed by a processor, the steps of the multi-beam sonar underwater terrain profile matching and positioning method as described in some of the above embodiments are implemented.

[0029] The beneficial effects brought by the technical solutions provided by the embodiments of the present application include:

[0030] By calculating the Zernike moments in the polar coordinate system based on the polar coordinate system data set and the matching point data set to construct their respective feature vector sets, and selecting the terrain template with the highest similarity to ensure accurate positioning, using the phase angle information of the feature vectors to correct the heading angle deviation, accurately adjusting the sounding point position and interpolating to optimize the inertial navigation indicated track; and by generating a set of parallel and symmetric reference sounding tracks, using the cross-correlation algorithm to dynamically adjust the spacing to minimize the similarity between the tracks, thereby obtaining a highly optimized track set, and finely matching this track set with the prior digital map, and selecting the center of the optimal track group as the basis for position estimation, significantly improving the accuracy and robustness of underwater terrain matching, and effectively solving the influence of the heading indication deviation on the positioning result. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic flowchart of an embodiment of the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0032] Figure 2 It is a schematic diagram for determining the sounding point and the search area of the reference sounding track in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0033] Figure 3 It is a schematic diagram of the process for determining the set of sounding points participating in the calculation in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0034] Figure 4 It is a schematic diagram of the formation of the matching template set in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0035] Figure 5 It is the intention of adjusting the direction of the matching track in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0036] Figure 6 It is a schematic diagram of the reference sounding track group and its corresponding underwater terrain profile line in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0037] Figure 7 It is a schematic diagram of the terrain profile line matching calculation in the multi-beam sonar underwater terrain profile matching and positioning method of the present application;

[0038] Figure 8 It is a schematic flowchart of another embodiment of the multi-beam sonar underwater terrain contour matching and positioning method of the present application;

[0039] Figure 9 It is a schematic diagram of the hardware structure of the multi-beam sonar terrain contour matching and positioning device involved in the embodiment solution of the present application. Specific embodiments

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0041] First, some technical terms in the present application are explained to facilitate the understanding of the present application by those skilled in the art.

[0042] To make the purpose, technical solution and advantages of the present application clearer, the embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0043] In a first aspect, an embodiment of the present application provides a multi-beam sonar underwater terrain contour matching and positioning method.

[0044] In one embodiment, with reference to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of the multi-beam sonar underwater terrain contour matching and positioning method of the present application. As Figure 1 shown, the multi-beam sonar underwater terrain contour matching and positioning method includes:

[0045] S100: Based on the sounding point data set and the matching point data set in the polar coordinate system, calculate the Zernike moments of different combinations, construct their respective feature vector sets, and use a metric index to evaluate the similarity. Select the matching point data with the highest similarity to the sounding point data set as the optimal matching and positioning template;

[0046] S200: Based on the feature vectors of the sounding point data set and the optimal matching and positioning template, retain the set of phase angles corresponding to the key feature vectors, calculate the average value of the angle difference to correct the heading angle deviation, and then adjust the positions of each sounding point and interpolate to obtain the corrected inertial navigation indicated track;

[0047] S300: Generate a set of reference sounding tracks that are parallel and symmetrically distributed according to the corrected inertial navigation indicated track, maintain an equal distance from the central track, evaluate the similarity between the terrain profiles corresponding to the tracks through the cross-correlation algorithm, adjust the distance according to the similarity feedback to optimize the tracks, and finally form an optimized track set;

[0048] S400: Perform a matching operation on the measured terrain contour line corresponding to the track set and the terrain contour corresponding to the array reference matching track on the prior digital map, and use the central track of the track group with the optimal evaluation index as the estimation basis for each position point in the current period.

[0049] In this embodiment, in S100, the Zernike moments of different combinations of the polar coordinate data set and the matching point data set are calculated to obtain the feature vector set of the polar coordinate data set and the feature vector set of the matching point data set; a metric is used to calculate the similarity of each group of feature vectors in the feature vector set of the polar coordinate data set and the feature vector set of the matching point data set, obtaining a matching similarity degree sequence of the two feature vector sets, and the terrain matching template corresponding to the maximum matching similarity degree is taken as the matching and positioning result. In S200, if the feature vector values of the polar coordinate data set and the feature vector values of the matching and positioning result are both greater than the set feature intensity, it indicates that the feature vector values of the two are the main terrain features, and the phase angles corresponding to the feature vector values of the two are retained respectively, thereby obtaining a set of phase angles where the feature vector values of the two are the main terrain features; based on the set of phase angles where the feature vector values of the polar coordinate data set and the matching and positioning result are the main terrain features, and calculating the angle difference between each pair of phase angles in the two sets of phase angles one by one, and taking the average value to obtain the heading angle deviation, thereby obtaining the heading angle deviation of each sounding point; based on the heading angle provided by the inertial navigation system for each sounding point, the heading angle deviation is corrected, and the corrected sounding points are obtained, thereby interpolating the sounding points on the corrected track line to obtain the inertial navigation indicated track after adjusting the direction angle. In S300, according to the inertial navigation indicated track after adjusting the heading, a plurality of parallel and left-right symmetrically distributed reference sounding tracks are determined, the spacing is kept consistent and the cross-correlation algorithm is used to evaluate the similarity. If the similarity does not meet the standard, the spacing is adjusted to optimize the reference track, and then a track set is obtained. Among them, if the data collected by adjacent tracks are highly similar (for example, repeatedly covering the same area), it will lead to a large amount of redundant data, wasting measurement time and resources. By dynamically adjusting the track spacing, it is ensured that the area covered by each track is as complementary as possible to the adjacent tracks. For example, the spacing is enlarged in flat areas and reduced in complex terrain areas, so as to cover a larger area with fewer tracks. In S400, the track set is used as a sliding window to be translated within the search area of the reference sounding track, obtaining an array of reference matching tracks; the measured terrain contour line corresponding to the track set is matched with the array of reference matching tracks on the prior digital map, and the center track of the track group with the optimal evaluation index is used as the estimation basis for each position point in the current period.

[0050] In summary, first, based on the polar coordinate dataset and the matching point dataset, the method constructs a feature vector set by calculating Zernike moments of different combinations, and uses a metric to evaluate the similarity of the feature vectors, so as to select the terrain matching template with the highest similarity as the final positioning result, ensuring the accuracy and robustness of terrain matching and laying a solid foundation for subsequent positioning work. Then, using the phase angle information in the feature vector, the course angle deviation is corrected by calculating the mean of the angle differences, effectively solving the problem of large course errors of the submersible in traditional methods and improving the accuracy of the sounding point position. Subsequently, interpolation is performed on the adjusted sounding point position to obtain the corrected inertial navigation indicated track, further improving the continuity and reliability of the track. Based on the corrected inertial navigation indicated track, the method generates a set of parallel and symmetrically distributed reference sounding tracks and maintains an equal spacing. The similarity between the tracks is evaluated by the cross-correlation algorithm, and the spacing is dynamically adjusted according to the similarity feedback to optimize the track set, ensuring the accuracy and consistency of the track set and providing high-quality input data for subsequent matching operations. Finally, the optimized track set is matched with the prior digital map. The optimal track group center track is selected through evaluation indicators as the estimation basis for each position point in the current time period, ensuring the accuracy and reliability of the positioning result and meeting the high-precision positioning requirements in practical applications.

[0051] Further, in one embodiment, before S100, the following steps are further included:

[0052] S000-1: Based on the effective measurement dataset of the multi-beam bathymetric sonar at the sounding point, bilinear interpolation is used to interpolate the effective measurement dataset to obtain a bathymetric dataset in the form of grid nodes. The bathymetric dataset in the form of grid nodes is normalized and subjected to a reference point transformation to obtain a bathymetric dataset symmetric about the grid center. Coordinate transformation is performed on the bathymetric dataset symmetric about the grid center to convert the Cartesian coordinate system to the polar coordinate system, and the dataset within the unit circle is taken to obtain the corresponding bathymetric point polar coordinate dataset.

[0053] In this embodiment, based on the effective measurement data set obtained by the multibeam bathymetric sonar at the sounding points, the bilinear interpolation method is used to interpolate these data. Through interpolation, the originally discrete measurement data is converted into a bathymetric data set in the form of continuous and uniform grid nodes, which facilitates subsequent processing. The bathymetric data set in the form of grid nodes after interpolation is normalized to eliminate the dimensional difference and numerical range difference between the data. Subsequently, a reference point transformation is performed to convert the bathymetric data set into a coordinate system with the center of the grid symmetry as the origin. Normalization helps to improve the stability and accuracy of subsequent algorithms, while the reference point transformation makes the data set more concentrated and symmetric, facilitating subsequent polar coordinate transformation, feature extraction, and matching operations. The bathymetric data set after normalization and reference point transformation is converted from the Cartesian coordinate system to the polar coordinate system. Then, according to actual requirements, the data set within the unit circle is taken as the object for subsequent processing. The data representation in the polar coordinate system is more suitable for describing circular or approximately circular terrain features and Zernike-related calculations. At the same time, by screening the data set within the unit circle, redundant information can be removed and the processing efficiency can be improved.

[0054] Further, in one embodiment, before S100, the following steps are further included:

[0055] S000-2: Determine a search sliding window based on the coordinates of the bathymetric data set symmetric about the grid center. Using the search sliding window as a template and the resolution of the prior digital map grid cell as the step size, slide within the matching search area of the sounding points to obtain a set of matching templates. Normalize, perform reference point transformation, and coordinate transformation on each matching template in the set of matching templates to obtain a data set of matching points in the polar coordinate system.

[0056] In this embodiment, first, according to the coordinates of the sounding point set in the bathymetric data set at the position of the grid symmetry center, a search sliding window is determined. This search sliding window will serve as the basic template for subsequent matching searches. Using the search sliding window as a template and the resolution of the prior digital map grid cell as the step size, perform a sliding search within the matching search area of the sounding points. In this way, all possible positions within the search area can be traversed to find the set of templates that match the search sliding window, that is, the set of matching templates. Normalize each matching template in the set of matching templates to eliminate the dimensional difference and numerical range difference between the data, perform a reference point transformation to convert the matching template into a coordinate system with a certain fixed point as the origin for subsequent processing. Finally, convert the matching template from the Cartesian coordinate system to the polar coordinate system to obtain a data set of matching points in the polar coordinate system.

[0057] Further, in one embodiment, after S400, the following steps are further included:

[0058] S500: Based on the estimated variance P of the inertial navigation position corrected at time tt 、The variance reduction threshold percentage η of inertial navigation position estimation 下 、The variance increase threshold percentage η of inertial navigation position estimation 上 and the variance P0 of the inertial navigation indicated position at the initial moment of terrain matching are used to control the number of beam formations of the multi-beam sonar.

[0059] In this embodiment, comprehensively considering the corrected variance P of the inertial navigation position estimation at time t t 、The variance reduction threshold percentage η of inertial navigation position estimation 下 、The variance increase threshold percentage η of inertial navigation position estimation 上 and the variance P0 of the inertial navigation indicated position at the initial moment of terrain matching, these four key parameters, according to the real-time changes of the above parameters, dynamically adjust the number of beam formations of the multi-beam sonar. By dynamically adjusting the number of beam formations, it is ensured that during the underwater terrain profile matching and positioning process of the multi-beam sonar, both high positioning accuracy can be maintained and the processing efficiency and energy consumption can be taken into account.

[0060] Further, in one embodiment, in S500, the following steps are further included:

[0061] S500-1: If the inertial navigation position variance P at time t t 、The variance reduction threshold percentage η of inertial navigation position estimation 下 and the variance P0 of the inertial navigation indicated position at the initial moment of terrain matching satisfy the formula P t ≤η 下 P0, then reduce the number of beam formations of the multi-beam sonar.

[0062] In this embodiment, when the formula P t ≤η 下 P0 is satisfied, it indicates that the current positioning accuracy is relatively high, and the number of beam formations can be appropriately reduced to reduce energy consumption and improve processing efficiency.

[0063] Further, in one embodiment, in S500, the following steps are further included:

[0064] S500-2: If the inertial navigation position variance P at time t t 、The variance increase threshold percentage η of inertial navigation position estimation 上 and the variance P0 of the inertial navigation indicated position at the initial moment of terrain matching satisfy the formula P t >η 上 P0, then increase the number of beam formations of the multi-beam sonar.

[0065] In this embodiment, when the formula P t >η 上 P0 is satisfied, it indicates that the current positioning accuracy may be affected, and the number of beam formations needs to be increased to improve the positioning accuracy and stability.

[0066] Further, in one embodiment, in S500, the following steps are further included:

[0067] S500-3: If the percentage η of the decrease threshold of the inertial navigation position estimation variance 下 and the percentage η of the increase threshold of the inertial navigation position estimation variance 上 satisfy the formula η 下 P0 > P t > η 上 P0 and η 上 ≥ 2η 下 , then the number of multi-beam sonar beam formations is maintained unchanged.

[0068] In this embodiment, when the formula η 下 P0 > P t > η 上 P0 and η 上 ≥ 2η 下 is satisfied, the number of beam formations of the multi-beam sonar should be maintained unchanged to avoid frequent switching of the sonar working mode.

[0069] In summary, this application relies on the rotation-invariant property of Zernike moments, uses the multi-beam measurement results at several measurement points on the terrain contour line to estimate and correct the terrain contour line angle, and reduces the influence of the heading angle error on the matching result; in terms of reducing the calculation amount, a method of using the measured terrain contour line data of several tracks adjacent to the inertial navigation indicated track to correct the main track is adopted to replace the strategy of continuous matching of the full bathymetric dataset; in terms of solving the continuous working energy consumption, based on the convergence degree of the inertial navigation positioning variance, a working mode switching threshold is set, and then the number of multi-beam sonar beam formations is regulated to achieve adaptive work to reduce energy consumption.

[0070] As Figure 8 shown in the process, here, the complete steps of this application are described as follows:

[0071] Step 1: Determine the matching search area

[0072] (1) Determine the matching search area of the sounding points

[0073] To avoid the huge calculation amount caused by global search on the data terrain library, the size of the matching search area is determined by the inertial navigation position estimation variance, as Figure 2 shown.

[0074] When the underwater terrain matching function of the submersible is turned on, after a period of measurement, the inertial navigation system indicates the track as S1, and X A , X B , X C are the starting point, middle point, and end point of the sounding of this section of the track respectively. Taking X A as an example, its coordinates are recorded as X A=(x A , y A ), and the position variance matrix of the inertial navigation at this moment is denoted as P A .

[0075]

[0076] Among them, is the eastward variance, is the northward variance sum, and is the covariance. The matching search area at this position can be calculated by the following formula:

[0077]

[0078] In the formula, a and b are the major and minor semi-axes of the position variance ellipse respectively, is the angle between the major semi-axis of the ellipse and the true north direction, L Ax , L Ay are the side lengths of the circumscribed rectangle of the position variance ellipse, and the calculation formulas are as follows:

[0079]

[0080] In the formula, is the expansion factor. (In practical applications, due to calculation errors, digital map errors, etc., to ensure that the position matching confidence level reaches 95%, should be taken.).

[0081] Taking the sounding point X A and the rectangle side lengths L Ax , L Bx to determine the rectangular area. To reduce the depth interpolation calculation amount, each side of the rectangle is extended outward so that the four vertices of the rectangle coincide with the nearest grid nodes on the corresponding digital map, and the area E1 enclosed by the dashed line in Figure 2 is the matching search area corresponding to the sounding position point X A . For other sounding position points X B , X C ,... on the track, the calculation process of the corresponding matching search area is the same as that of X A .

[0082] (2) Determine the search area of the reference sounding track

[0083] The sounding data corresponding to each sounding point on the track S1 can form an underwater terrain contour line, as shown in the right figure of Figure 2 . The possible distribution range of this contour line on the prior digital map is the search area of the reference sounding track. The specific determination method is: determine the set of matching search areas {E1, E2,... E k} corresponding to each sounding point on the track S1, and determine the rectangular area G1 on the prior digital map so that it satisfies See Figure 2 as shown by G1 in

[0084] Step 2: Adjustment of Terrain Matching Profile Direction

[0085] The terrain profile matching method has the characteristics of small computational complexity and simple implementation, but it is sensitive to the heading error of the navigation indicated track. Therefore, the present invention utilizes the rotation invariance of the terrain Zernike moment to correct the navigation indicated track, reducing the influence of the heading error on the accuracy of subsequent terrain profile matching. The specific process is as follows:

[0086] (1) Determine the sounding point set participating in the calculation

[0087] Taking the sounding point X A as an example, if at this point, the effective measurement data set (abnormal values with too large errors are screened out) of the multi-beam sounding sonar is H XA ={p A1 , p A2 , p A3 … p Ak}, where p A1 =(x′ A1 , y′ A1 , h A ′1) is the position coordinates x′ A1 , y′ A2 and the depth h A ′3 of the measurement point A1. Use the bilinear interpolation method to interpolate the sounding data set to obtain a data set in the form of grid nodes and perform normalization processing on each coordinate position. Taking the normalization processing of the coordinates corresponding to p A1 as an example, if the normalized coordinates of x′ A1 , y′ A1 are x″ A1 , y″ A2 , the calculation formula is as follows:

[0088]

[0089] Perform a reference point transformation on the normalized data set. The reference point (origin) is converted from the lower left corner to the grid symmetry center position to obtain the point set H′ XA . On this basis, perform a coordinate transformation, converting from the Cartesian coordinate system to the polar coordinate representation form, and take the point set H″ XA inside the unit circle. The transformation formula is as follows,

[0090]

[0091]

[0092] Then, the sounding point X AThe corresponding multi-beam measurement set is H″ XA ={(ρ A1 ,θ A1 ,h A1 ),…(ρ Ak ,θ Ak ,h Ak )}, where |ρ| ≤ 1, as Figure 3 shown.

[0093] (2) Determine the data set participating in the direction adjustment matching calculation

[0094] E1 is the matching search area corresponding to the sounding point X A . According to the coordinates of the sounding position point set in the data set H′ XA {(x′ A1 ,y′ A1 ),…,(x′ Ak ,y′ Ak ), determine the search sliding window U A . The lower left and upper right vertex coordinates of the sliding window are (x 左 ,y 左 ), (x 右 ,y 右 ).

[0095]

[0096] Using U A as a template, with the prior digital map grid cell resolution ω as the step size, slide within the sounding point matching search area E1 to obtain the matching template set {U A1 ,U A2 ,…,U Au}, as Figure 4 shown.

[0097] According to the method in (2), normalize, perform reference point transformation, and coordinate transformation on each matching template in the set {U A1 ,U A2 ,…,U Au}, and take |ρ| ≤ 1 to obtain the matching point data set {U′ A1 ,U′ A2 ,…,U′ Au}.

[0098] (3) Calculate the Zernike moments of the sounding point and the matching point data set

[0099] Calculate the n-th order and m-th degree Zernike moments of the sounding point set H″ XA and the matching point data set {U′ A1 ,U′ A2 ,…,U′ Au}. The calculation formula is as follows.

[0100]

[0101] In the formula, n is a positive integer or zero, and m is a positive integer or a negative integer. The relationship between m and n is that n - |m| is an even number, and n ≥ |m|. R nm (ρ) represents the radial polynomial of the position point (x, y), and the calculation formula is as follows:

[0102]

[0103] The expressions of each order of Zernike polynomials (normalized) in polar coordinates can be obtained by looking up tables. This content is well-known and will not be elaborated here.

[0104] (4) Construct the feature vector sets of the sounding point and matching point data sets

[0105] Using the method in (3), for the Zernike moments F of different combinations of n and m in the sounding point set and the matching point data set n,m calculate, where n and m need to satisfy the constraint conditions in (3). Use the calculation results to construct the feature vector XA of the sounding point set H″ and the feature vector set of the matching template set {U′ A1 , U′ A2 , …, U′ Au} Among them, n and m can be quantitatively determined according to the terrain undulation degree in the prior map of the matching area. Generally, in areas with frequent terrain changes, the value of n should be larger to better depict the local details of the terrain; on the contrary, the value can be smaller to only represent the overall change trend of the terrain. For areas with more terrain similarities, the value of n should be larger to increase the discrimination degree of the feature set during the matching calculation.

[0106] (5) Matching calculation of the sounding point and matching point data sets

[0107] Perform similarity calculation on the feature vector XA of the sounding point set H″ and each group of feature vectors in the matching point data feature vector set . The selectable measurement indexes include Euclidean distance, Mahalanobis distance, hausdorff distance, Canberra distance, etc. Among them, the Euclidean distance is simple to calculate, but does not consider the influence of the change scale on different eigenvalues. The present invention adopts the Canberra distance, and this measurement index can overcome the influence of the dimension on the calculation result. The specific calculation formula is as follows:

[0108]

[0109] Among them, and respectively represent and the i-th feature in. By calculating, the matching similarity degree sequence D = {d1, d2,... d u} of the sounding point and the feature vectors of the matching point dataset is obtained. Take the d max = max{d1, d2,... d u} corresponding matching template set {U′ A1 , U′ A2 ,..., U′ Au} as the matching positioning result.

[0110] (6) Heading Angle Deviation Estimation

[0111] If the phase angles corresponding to the features formed by different combinations of n and m in the sounding vector are a′ n,m , and the phase angles corresponding to the features formed by different combinations of n and m in the matching positioning result are a n,m . is the set threshold of the feature intensity (Zernike moment intensity). Compare it with the values of each feature vector in U′ Amax . If and , it indicates that this feature is the main terrain feature, and the corresponding phase angles a n,m and a′ n,m are retained. Otherwise, they are excluded, obtaining two sets of phase angle sets of the main features, each containing K phase angles

[0112]

[0113] Calculate the angle differences of each pair of phase angles in the set one by one, and take the average value to obtain the heading angle deviation

[0114]

[0115] (7) Inertial Navigation Indicator Track Direction Adjustment

[0116] Use the heading angle deviations A , X B , X C of the measurement position points calculated by the above (1)-(6). The heading angles of the three points X provided by the inertial navigation system are respectively denoted as θ A , θ B , θ C 、θ A 、θB 、 θ C , the heading angle deviation is corrected, and the formula is as follows.

[0117]

[0118] Calculate the corrected measured position point X' B and X' C , interpolate the sounding points on the corrected track according to the sounding frequency and inertial navigation rate information, and obtain the inertial navigation indicated track S'1={X A ,…X' B …X' C} after adjusting the direction angle, as Figure 5 shown.

[0119] Step 3: Terrain matching contour line correction

[0120] (1) Determine the number of reference sounding tracks

[0121] Taking the indicated track S'1 after adjusting the heading as a template, determine several reference sounding tracks parallel to S'1 in the sounding results of the multibeam echosounder Among them, the number of reference sounding tracks should be determined according to the coverage range of the multibeam sonar survey area and the number of underwater terrain features. To improve the accuracy of terrain contour matching correction, the selected reference sounding tracks should be symmetrically distributed left and right with the inertial navigation indicated track S'1 (the corresponding terrain contour line is directly below the submersible) as the midline. Usually, K is an even number. If the multibeam sonar survey area is large and the underwater terrain features are rich, the value of K should be increased; otherwise, the value of K should be decreased. In this embodiment, K = 2 is used for illustration. If the inertial navigation indicated track of the submersible is S'1, use the underwater terrain fitted by the multibeam sounding data to determine 2 reference sounding tracks with a distance D from S'1 The corresponding underwater terrain contour line is As Figure 6 shown.

[0122] The distance D is usually taken as an integer multiple of the digital map grid resolution, and the value of D can be determined by the similarity degree between the reference sounding track and the terrain contour corresponding to the inertial navigation indicated track. If the similarity degree is less than the threshold J COR , it means that the reference sounding track provides less terrain information, and the value of D should be changed to obtain a terrain contour that is beneficial to improving the matching efficiency.

[0123] (2) Calculate the similarity between the reference sounding track and the inertial navigation indicated track

[0124] The contour similarity degree J COR can be calculated by the cross-correlation algorithm (COR), and the formula is as follows:

[0125]

[0126] Among them, n is the total number of corresponding depth measurement points on the track, and h′1(i) is the i-th depth measurement point on the track S′1. For the track the i-th depth measurement point, and a more effective reference sounding track is obtained by adjusting the value of D. Generally, the smaller the value of J COR is, the greater the difference in the corresponding terrain contour lines between the two tracks, which is more conducive to subsequent terrain feature matching.

[0127] (3) Terrain contour line matching calculation

[0128] Using the track set determined in (2) as a sliding window to translate within the search area G1 of the reference sounding track, an array of reference matching tracks is obtained, as Figure 7 shown.

[0129] The measured terrain contour line corresponding to the track set is matched with the terrain contour line corresponding to the reference matching track on the prior digital map to obtain a matching evaluation index Among them, K represents the number of reference matching tracks obtained by translation. The matching evaluation indexes of each terrain contour can be calculated with reference to the typical index formula of terrain contour matching, which will not be elaborated here.

[0130] The weighted average of each group of indexes is obtained to get

[0131]

[0132] Among them, the weight values a1, a2, a3 characterize the credibility of the multi-beam sonar sounding data and are related to factors such as the attitude and depth of the submersible. The result of the terrain contour matching of the submersible this time is the central track of the best evaluation index track group and this track is used as the estimation basis for each position point in the current time period.

[0133] Step 4: Judgment of sonar working mode switching

[0134] When the underwater vehicle is in the terrain matching stage, the purpose of estimating its own track points will be achieved by repeatedly executing Steps 1 - 3. Since it is difficult to achieve accurate sub-grid level track estimation with the terrain contour matching method based on digital map grids, after a period of position estimation, when the matching result of the algorithm converges to a certain extent, the effect of the matching result on correcting the inertial navigation positioning error is greatly reduced.

[0135] Therefore, the inertial navigation position estimation variance P after correction can be used t ​Based on this, the sonar working mode is controlled, η 下 and η 上 are the descending and ascending threshold percentages of the position variance, where 1 > η 上 > η 下 > 0. To prevent the sonar working mode from switching frequently, η 上 ≥ 2η 下 , for example, η 下 = 0.4, η 下 = 0.8. After terrain matching correction for a period of time, at time t, the inertial navigation position variance satisfies P t ≤ η 下 P0, reducing the number of multi-beam sonar beam formations, achieving the purpose of reducing device power consumption at the cost of a certain sounding accuracy; when P t > η 上 P0, increasing the number of multi-beam sonar beam formations, restoring the sounding accuracy to improve the accuracy of matching estimation. Among them, P0 is the variance of the inertial navigation indicated position at the initial moment of terrain matching.

[0136] In a second aspect, the embodiments of the present application further provide a multi-beam sonar terrain contour matching and positioning device. The multi-beam sonar terrain contour matching and positioning device includes: a feature vector construction and terrain matching module, which is used to construct respective feature vector sets by calculating Zernike moments of different combinations based on the polar coordinate data set and the matching point data set, evaluate the similarity using a metric index, and select the terrain matching template with the highest similarity as the final positioning result; a heading angle deviation correction and track adjustment module, which is used to retain the set of phase angles corresponding to the key feature vectors based on the polar coordinate data set and the feature vectors of the matching positioning result, calculate the average value of the angle differences to correct the heading angle deviation, and then adjust the sounding point position and interpolate to obtain the corrected inertial navigation indicated track; a track generation and optimization module, which is used to generate a set of parallel and symmetrically distributed reference sounding tracks according to the corrected inertial navigation indicated track, maintain equal spacing and evaluate the similarity between the tracks through the cross-correlation algorithm, and adjust the spacing according to the similarity feedback to optimize the tracks, and finally form an optimized track set; a terrain contour matching and final positioning module, which is used to perform a matching operation on the measured terrain contour line corresponding to the track set and the array reference matching tracks on the prior digital map, and use the central track of the track group with the optimal evaluation index as the estimation basis for each position point in the current period.

[0137] Among them, the function implementation of each module in the above multi-beam sonar terrain contour matching and positioning device corresponds to each step in the embodiment of the above multi-beam sonar underwater terrain contour matching and positioning method, and its function and implementation process will not be elaborated here one by one.

[0138] In a third aspect, an embodiment of the present application provides a multi-beam sonar terrain contour matching and positioning device, which may be a device with data processing capabilities such as a personal computer (PC), a laptop computer, an edge computing device, etc.

[0139] Referring to Figure 9 , Figure 9 FIG. is a schematic diagram of the hardware structure of the multi-beam sonar terrain contour matching and positioning device involved in the solution of the embodiment of the present application. In the embodiment of the present application, the multi-beam sonar terrain contour matching and positioning device may include a processor, a memory, a communication interface, and a communication bus.

[0140] Among them, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0141] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, etc., which are used to implement the interconnection of components inside the multi-beam sonar terrain contour matching and positioning device, and interfaces for interconnecting the multi-beam sonar terrain contour matching and positioning device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display screen, a keyboard, etc.

[0142] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0143] The processor can be a general-purpose processor, which can call the multi-beam sonar terrain contour matching and positioning program stored in the memory and execute the multi-beam sonar underwater terrain contour matching and positioning method provided by the embodiment of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the multi-beam sonar terrain contour matching and positioning program is called can refer to the various embodiments of the multi-beam sonar underwater terrain contour matching and positioning method of the present application, which will not be elaborated here.

[0144] Those skilled in the art can understand that Figure 9 the hardware structure shown in Figure 9 does not constitute a limitation to this application. It may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0145] Fourthly, the embodiments of this application also provide a readable storage medium.

[0146] The readable storage medium of this application stores a multi-beam sonar terrain contour matching and positioning program. When the multi-beam sonar terrain contour matching and positioning program is executed by a processor, the steps of the multi-beam sonar underwater terrain contour matching and positioning method as described above are implemented.

[0147] Among them, the method implemented when the multi-beam sonar terrain contour matching and positioning program is executed can refer to the various embodiments of the multi-beam sonar underwater terrain contour matching and positioning method of this application, which will not be elaborated here.

[0148] It should be noted that the serial numbers of the above embodiments of this application are only for description and do not represent the superiority or inferiority of the embodiments.

[0149] The terms "including" and "having" and any variations thereof in the specification, claims and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices. The descriptions of the terms "first", "second", "third", etc. are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are of different types.

[0150] In the description of the embodiments of this application, terms such as "exemplary", "for example" or "for instance" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0151] In the description of the embodiments of this application, unless otherwise specified, " / " means "or". For example, A / B may represent A or B; "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "a plurality of" means two or more than two.

[0152] In some processes described in the embodiments of the present application, multiple operations or steps appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0154] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A multi-beam sonar underwater terrain contour matching positioning method, characterized in that: The multi-beam sonar underwater terrain contour matching positioning method comprises: Based on the bathymetric point data set and matching point data set in the polar coordinate system, the Zernike moments of different combinations are calculated, the respective feature vector sets are constructed, and the similarity is evaluated using the metric index. The matching point data with the highest similarity to the bathymetric point data set is selected as the optimal matching positioning template. Based on the feature vectors of the sounding point data set and the best matching positioning template, the phase angle set corresponding to the key feature vector is retained, the angle difference mean is calculated to correct the heading angle deviation, and then the position of each sounding point is adjusted and interpolated to obtain the corrected inertial navigation indication track; According to the corrected inertial navigation indicated track, a set of parallel and symmetrically distributed reference bathymetric tracks are generated, which are kept equidistant from the central track and the similarity between the terrain contours corresponding to the tracks is evaluated through the cross-correlation algorithm. The spacing is adjusted according to the similarity feedback to optimize the track, and finally an optimized track set is formed; The measured terrain contour lines corresponding to the track set are matched with the terrain contours corresponding to the array reference matching tracks on the prior digital map, and the center track of the track group with the best evaluation index is used as the estimation basis for each position point in the current period.

2. The multi-beam sonar underwater terrain contour matching positioning method according to claim 1, characterized in that: Before the method of calculating the Zernike moments of different combinations of the two sets in the polar coordinate system based on the bathymetric point data set and the matching point data set in the polar coordinate system, constructing respective feature vector sets, and evaluating the similarity using a metric index, and selecting the matching point data with the highest similarity to the bathymetric point data set as the optimal matching positioning template, the method further includes: Based on the effective measurement data set of multi-beam bathymetric sonar at the sounding point, the bilinear interpolation method is used to interpolate the effective measurement data set to obtain a bathymetric data set in the form of grid nodes, and the bathymetric data set in the form of grid nodes is normalized and the reference point is transformed to obtain a bathymetric data set with grid center symmetry. The bathymetric data set with grid center symmetry is transformed by coordinate transformation, the Cartesian coordinate system is converted into the polar coordinate system, and the data set within the unit circle is taken to obtain the corresponding data set of the bathymetric point polar coordinate system.

3. The multi-beam sonar underwater terrain contour matching positioning method according to claim 1, characterized in that: Before the method of calculating the Zernike moments of different combinations in the bathymetric point data set and the matching point data set in the polar coordinate system to construct respective feature vector sets, and using a metric to evaluate the similarity, and selecting the matching point data with the highest similarity to the bathymetric point data set as the optimal matching positioning template, the method further includes: The search sliding window is determined based on the coordinates of the bathymetric data set that is symmetrical based on the grid center. The search sliding window is used as a template and the resolution of the grid cells of the prior digital map is used as the step size. The search sliding window is slid in the matching search area of ​​the bathymetric point to obtain a matching template set. Each matching template in the matching template set is normalized, the reference point is transformed, and the coordinates are converted to obtain a matching point data set in the polar coordinate system.

4. The multi-beam sonar underwater terrain contour matching positioning method according to claim 1, characterized in that: After matching the measured terrain contour lines corresponding to the track set with the terrain contour lines corresponding to the array reference matching tracks on the prior digital map, and taking the center track of the track group with the best evaluation index as the estimation basis for each position point in the current period, the method further includes: Based on the corrected inertial navigation position estimation variance P at time t t , Inertial navigation position estimation variance decrease threshold percentage η 下 , Inertial navigation position estimation variance rising threshold percentage η 上 The inertial navigation position variance P0 at the initial moment of terrain matching is used to control the number of multi-beam sonar beamforming.

5. The multi-beam sonar underwater terrain contour matching positioning method according to claim 4, characterized in that: The inertial navigation position estimation variance P after correction at time t t , Inertial navigation position estimation variance decrease threshold percentage η 下 , Inertial navigation position estimation variance rising threshold percentage η 上 The inertial navigation position variance P0 at the initial moment of terrain matching controls the number of multi-beam sonar beamforming, including: If the inertial navigation position variance P at time t t , Inertial navigation position estimation variance decrease threshold percentage η 下 The inertial navigation position variance P0 at the initial moment of terrain matching satisfies the formula P t ≤η 下 P0, then reduce the amount of multibeam sonar beamforming.

6. The multi-beam sonar underwater terrain contour matching positioning method according to claim 4, characterized in that: The inertial navigation position estimation variance P after correction at time t t , Inertial navigation position estimation variance decrease threshold percentage η 下 , Inertial navigation position estimation variance rising threshold percentage η 上 The inertial navigation position variance P0 at the initial moment of terrain matching controls the number of multi-beam sonar beamforming, including: If the inertial navigation position variance P at time t t , Inertial navigation position estimation variance rising threshold percentage η 上 The inertial navigation position variance P0 at the initial moment of terrain matching satisfies the formula P t >η 上 P0, then increase the number of multibeam sonar beamforming.

7. The multi-beam sonar underwater terrain contour matching positioning method according to claim 4, characterized in that: The inertial navigation position estimation variance P after correction at time t t , Inertial navigation position estimation variance decrease threshold percentage η 下 , Inertial navigation position estimation variance rising threshold percentage η 上 The inertial navigation position variance P0 at the initial moment of terrain matching controls the number of multi-beam sonar beamforming, including: If the variance of the inertial navigation position estimation drops by a threshold percentage η 下 , Inertial navigation position estimation variance rising threshold percentage η 上 Satisfy the formula η 下 P0>P t >η 上 P0 and η 上 ≥2η 下 , the number of multi-beam sonar beamforming is maintained unchanged.

8. A multi-beam sonar terrain contour matching positioning device, characterized in that: The multi-beam sonar terrain contour matching and positioning device comprises: The feature vector construction and terrain matching module is used to construct the respective feature vector sets based on the bathymetric point dataset and the matching point dataset in the polar coordinate system by calculating the Zernike moments corresponding to different combinations, and use the metric index to evaluate the similarity, and select the matching point data with the highest similarity to the bathymetric point dataset as the optimal matching positioning template; The heading angle deviation correction and track adjustment module is used to retain the phase angle set corresponding to the key feature vector based on the sounding point data set in the polar coordinate system and the feature vector of the optimal matching positioning template, calculate the angle difference mean to correct the heading angle deviation, and then adjust the sounding point position and interpolate to obtain the corrected inertial navigation indication track; Track generation and optimization module, which is used to generate a set of parallel and centrally symmetrically distributed reference bathymetric tracks based on the corrected inertial navigation indicated tracks, keep the same spacing with the central track and evaluate the similarity between the terrain contours corresponding to the tracks through the cross-correlation algorithm, adjust the spacing according to the similarity feedback to optimize the tracks, and finally form an optimized track set; The terrain contour matching and final positioning module is used to match the measured terrain contour lines corresponding to the track set with the terrain contour lines corresponding to the array reference matching tracks on the prior digital map, and use the center track of the track group with the best evaluation index as the estimation basis for each position point in the current period.

9. A multi-beam sonar terrain contour matching positioning device, characterized in that: The multi-beam sonar terrain profile matching and positioning device includes a processor, a memory, and a multi-beam sonar terrain profile matching and positioning program stored in the memory and executable by the processor, wherein when the multi-beam sonar terrain profile matching and positioning program is executed by the processor, the steps of the multi-beam sonar underwater terrain profile matching and positioning method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a multi-beam sonar terrain contour matching and positioning program, wherein when the multi-beam sonar terrain contour matching and positioning program is executed by the processor, the steps of the multi-beam sonar underwater terrain contour matching and positioning method as described in any one of claims 1 to 7 are implemented.

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