CN113686336B

CN113686336B - Method for improving underwater navigation precision based on grid topological structure iterative optimal ring domain point

Info

Publication number: CN113686336B
Application number: CN202110738636.6A
Authority: CN
Inventors: 郑伟; 李钊伟; 赵世杰
Original assignee: China Academy of Space Technology CAST
Current assignee: China Academy of Space Technology CAST
Priority date: 2021-06-30
Filing date: 2021-06-30
Publication date: 2024-05-14
Anticipated expiration: 2041-06-30
Also published as: CN113686336A

Abstract

The invention discloses a method for improving underwater navigation precision based on grid topological structure iteration optimal ring domain points, which comprises the following steps: obtaining an optimal matching position of the track starting point of the underwater vehicle through a track starting point small loop domain grid matching positioning strategy; generating lattice points to be matched of a large loop domain according to the optimal matching position of the track starting point of the underwater vehicle and through a three-layer loop domain matching positioning strategy of the track end point changing angle; and iteratively calculating the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and obtaining the optimal matching position of the underwater vehicle track end point in the large loop domain according to the optimal principle so as to realize the effective matching and positioning of the underwater vehicle track end point, further correcting the control parameters of the INS system and assisting in completing the navigation target of the underwater vehicle with long endurance and long range. By the method, the matching precision of the underwater vehicle gravity assisted navigation is improved.

Description

Method for improving underwater navigation precision based on grid topological structure iterative optimal ring domain point

Technical Field

The invention belongs to the crossing technical field of underwater navigation, marine surveying and mapping, and the like, and particularly relates to a method for improving underwater navigation precision based on grid topological structure iteration optimal ring domain points.

Background

The inertial navigation system (Inertial Navigation System, INS) is a core navigation system for realizing autonomous navigation of the underwater vehicle, and has a short-time high-precision positioning characteristic, but inherent errors of inertial components, multiple components of positioning solution and the like cause the INS errors to be accumulated and diverged with time so as to be difficult to meet a positioning target with high precision during long-term navigation of the underwater vehicle, so that the INS needs to be calibrated regularly to maintain the navigation precision.

The gravity field information which is one of the inherent geographic attributes of the earth is not easily influenced by uncertain environments such as climate, sea wave and the like and shows long-time relative stability, so the gravity field information is suitable for being used for assisting navigation, and the gravity assisted navigation system is currently used as an important technology for assisting INS navigation underwater and becomes an international hot spot subject for study of domestic and foreign scholars.

The matching algorithm is the core of a gravity assisted inertial navigation system, and the currently common gravity matching algorithm mainly comprises a Mordy inertia terrain assisted navigation algorithm SITAN, an iterative nearest contour point algorithm ICCP and a terrain contour matching algorithm TERCOM. In comparison, the TERCOM algorithm has obtained extensive attention and research of students with the advantages of simple calculation, insensitivity to initial errors, strong robustness, higher positioning accuracy and the like.

In the aspect of improving the matching precision of the TERCOM algorithm, liu and the like propose a novel INS/TERCOM system optimization structure which is formed by combining coordinate transformation, mismatching detection and Kalman filtering, and research the matching performance of a distributed type and self-adaptive federal filtering information fusion algorithm; yan et al propose a new matching algorithm by integrating TERCOM and ICCP, obtain an initial position with TERCOM and accurately locate with ICCP; yuan et al propose a combined underwater auxiliary navigation algorithm by fusing a Kalman filter algorithm with a TERCOM/ICCP algorithm, and meanwhile, the accuracy of accurate matching adopts a sliding window to improve the algorithm efficiency; wang et al propose a rotation splice gravity matching algorithm based on the TERCOM algorithm; tong et al approximates the local gravity reference diagram by a two-dimensional Gaussian basis function, solves a relevant extremum matching model by a quasi-Newton BFGS nonlinear optimizing method to provide a combined matching algorithm based on the local gravity map approximation, and improves the matching precision of the algorithm by rough matching of TERCOM and preprocessing of measured data by a difference method; zhao et al combine the TERCOM algorithm with particle filtering to propose a new terrain aided navigation algorithm to enhance the positioning accuracy of BITAN II algorithm; wei et al propose a correlation SITAN algorithm based on weighted decreasing iteration to solve the problems of initial errors and linear errors, while using TERCOM for the correlation process of the algorithm; zhang et al provides an online judgment criterion of multi-reference point joint probability mismatching in a relevant plane for solving the mismatching problem that a TERCOM algorithm is susceptible to altitude measurement errors, terrain similarity and the like; wang et al realize the noiseless nano positioning of the deepest part of the earth ocean through a TERCOM algorithm under the support of sea bucket data; zhang et al utilize TERCOM algorithm simulation analysis to obtain the influence law of main factors such as submersible vehicle speed, sounding precision, initial position deviation, underwater topography characteristics, digital map resolution and the like on matching precision. In the aspect of improving the matching efficiency and reliability of the TERCOM algorithm, han and the like merge a shortest path algorithm and a new correlation analysis algorithm to construct an improved TERCOM algorithm, and simultaneously, a new matching algorithm under a mismatching diagnosis method is provided by a spatial sequence constraint and decision criterion limiting integration mechanism so as to improve the reliability and matching precision of the TERCOM; liu Xianpeng and the like track the course of the submersible by using the speed and course information output by the INS to provide a TERPM positioning algorithm based on the course tracking; liwei and the like propose a novel hierarchical neighborhood threshold search method based on a coarse-fine matching strategy to improve the matching efficiency of the point-by-point traversal search of a TERCOM algorithm; li and the like are coupled with the attitude control theory in the air-sea environment through the shortest arc principle of spherical geometry, so that a novel geodesic method is provided to reduce the scale of a matching area and improve the matching efficiency of an algorithm; zhang et al uses TERCOM as a line matching algorithm to carry out surface matching by geometric similarity so as to provide a line-surface combined underwater topography matching algorithm, thereby improving the robustness and positioning accuracy of the algorithm.

In summary, most scholars develop research mainly around the application of TERCOM and the improvement of navigation performance of underwater vehicles, and there is little research work on changing the topology of the TERCOM matching grid. However, the method is limited by the traditional TERCOM algorithm, is not dependent on the position information of the track starting point, takes only 3 times of INS error at the track ending point as a half-side length stretch square grid matching lattice, and traverses the search to determine the optimal matching positioning of the underwater vehicle position, but the search mechanism has large calculation amount and is easy to cause low algorithm matching efficiency; in addition, the TERCOM algorithm has the defects of difficult effective treatment of observation noise and process noise, sensitivity to angle errors of inertial navigation segments and the like, so that the design problem different from TERRCOM matching grid topology needs to be further discussed.

Disclosure of Invention

The technical solution of the invention is as follows: the method and the system for improving the underwater navigation precision based on the grid topological structure iterative optimal ring domain point are provided, and aim to improve the matching precision of the underwater vehicle gravity assisted navigation.

In order to solve the technical problems, the invention discloses a method for improving underwater navigation precision based on iterative optimal ring domain points of a grid topological structure, which comprises the following steps:

obtaining an optimal matching position of the track starting point of the underwater vehicle through a track starting point small loop domain grid matching positioning strategy;

generating lattice points to be matched of a large loop domain according to the optimal matching position of the track starting point of the underwater vehicle and through a three-layer loop domain matching positioning strategy of the track end point changing angle;

And iteratively calculating the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and obtaining the optimal matching position of the underwater vehicle track end point in the large loop domain according to the optimal principle so as to realize the effective matching and positioning of the underwater vehicle track end point, further correcting the control parameters of the INS system and assisting in completing the navigation target of the underwater vehicle with long endurance and long range.

In the method for improving underwater navigation accuracy based on the grid topological structure iterative optimal ring domain point, the construction flow of the small ring domain grid matching positioning strategy of the track starting point is as follows:

Acquiring an INS track output inertial navigation sequence { S ₁,S₂,…,S_L } of a certain section of underwater vehicle navigation according to a sampling time interval deltat; wherein L represents the length of a sampling sequence of an INS track, and S ₁、S₂、…、S_L represents each track point in an inertial navigation sequence;

Extracting position coordinates and measured gravity values corresponding to each track point from { S ₁,S₂,…,S_L }, and constructing a position coordinate sequence { (x ₁,y₁),(x₂,y₂),…,(x_L,y_L) } and a measured gravity value sequence { g ₁,g₂,…,g_L } of the underwater vehicle;

taking the position coordinate (x ₁,y₁) of a track starting point S ₁ in the inertial navigation sequence as a center and taking 3 sigma ₀ as the searching boundary radius of the maximum extension, and constructing to obtain a small loop domain; wherein σ ₀ represents the standard deviation of inertial navigation drift error when the sampling interval is Δt;

And determining the total number and position coordinates of grid points to be matched in the small ring domain according to the north-east rotation angle theta ₀ and the ring radius proportion average coefficient lambda, and constructing to obtain a grid point set to be matched in the small ring domain.

In the method for improving underwater navigation accuracy based on iterative optimal ring domain points of grid topology structure, the position coordinates of each grid point to be matched in the small ring domainThe calculation formula of (2) is as follows:

Wherein, And/>Respectively, the abscissa of the j-th lattice point on the i-th small ring, r _i represents the radius of the i-th ring in the small ring domain, and beta _j represents the rotation angle of the j-th lattice point on each ring in the small ring domain.

In the method for improving the underwater navigation accuracy based on the iterative optimal ring domain point of the grid topology structure,

r_i＝3σ₀λi

β_j＝j·θ₀

Wherein i=1, 2, …, and the maximum value of iAnd maximum value of j

In the method for improving the underwater navigation precision based on the grid topological structure iterative optimal ring domain point, the optimal matching position of the track starting point of the underwater vehicle is obtained through the small ring domain grid matching positioning strategy of the track starting point, and the method comprises the following steps:

the position coordinates (x ₁,y₁) of the track starting point S ₁ and the position coordinates of each lattice point to be matched in the small ring are calculated A point set to be matched, which is used as a true position of a track starting point;

mapping a point set to be matched of the true position of the track starting point on a gravity reference map point by point, and obtaining a theoretical gravity value corresponding to (x ₁,y₁) according to the gravity value at the nearest gravity reference grid point as the gravity value of the point to be matched And/>Corresponding theoretical gravity value/>

Wherein, C represents the grid resolution of the gravity reference graph, mapt (·, ·) represents the gravity value matrix of the gravity reference graph at the grid point positions, [ · ] represents rounding;

obtaining the optimal matching position of the track starting point of the underwater vehicle according to the principle of minimizing the absolute value of the gravity deviation

Wherein, when i=0 and j=0,Namely x ₁,/>I.e. y ₁.

In the method for improving underwater navigation accuracy based on the grid topological structure iterative optimal ring domain point, the construction flow of the track end point variable angle three-layer ring domain matching positioning strategy is as follows:

Determining and obtaining course information and range information of the underwater vehicle navigation according to the position coordinates (x ₁,y₁) of the track starting point S ₁ and the position coordinates (x _L,y_L) of the track ending point S _L in the inertial navigation sequence:

wherein d _INS represents the distance measurement between the start point and the end point of the inertial navigation track which is marked by h epsilon [0, + ] norm, and setting h=2, namely calculating Euclidean distance; alpha _INS represents a heading radian angle in a positive direction with the abscissa of the coordinate system;

According to And course information and range information of the underwater vehicle navigation, estimating and obtaining central position coordinates (x _O,y_O) of the large ring domain:

Taking (x _O,y_O) as a center, taking R _max as the maximum extension of the search boundary radius, constructing and obtaining a variable-angle three-layer topological structure annular grid point region covering the true end point of the track, and recording the variable-angle three-layer topological structure annular grid point region as a large annular domain;

Taking the grid resolution C of the gravity reference diagram as the span interval of each ring of the large ring domain to obtain the total ring number of the large ring domain

Grid resolution C of gravity reference diagram and intermediate ringIs used as the reference angle/>, generated by each grid point in the large ring domainAccording to the principle of 'inner-multiple outer-half', determining the span angle between adjacent lattice points on the inner layer ring of the large ring domain as/>The span angle between adjacent lattice points on the outer layer ring is/>Combining the total number M of rings to construct a large-ring-domain grid point set to be matched; the large-ring domain grid point set to be matched is of a variable-angle three-ring layer grid point topological structure.

In the method for improving underwater navigation accuracy based on the iterative optimal ring domain points of the grid topology structure, the generation mode of the large ring domain lattice points to be matched is as follows:

The total number of macrocycles under the three mechanisms 1 sigma-EPMP, 2 sigma-EPMP and 3 sigma-EPMP are respectively recorded as Wherein sigma represents the standard deviation of inertial navigation accumulated drift error;

For a pair of Respectively correcting to obtain the total number M' ₁、M′₂、M′₃ of the large ring domain under the three corrected mechanisms:

Wherein, ζ=1, 2,3 corresponds to 1σ -EPMP mechanism, 2σ -EPMP mechanism, 3σ -EPMP mechanism, respectively;

and obtaining the radius R _1,j、R_2,j、R_3,j of each ring of the large ring domain under the three mechanisms according to the corrected total number M ₁′、M₂′、M₃' of the large ring domain under the three mechanisms:

R_ξ,ζ＝ζC,ζ＝1,2,…,M′_ξ···(6)

Wherein R _ξ,ζ represents the radius of the ζ ring of the macrocyclic domain under the ζ mechanism;

obtaining the intermediate ring radius of the large ring domain under the three mechanisms according to the total number M' ₁、M′₂、M′₃ of the large ring domain under the three mechanisms after correction

Intermediate ring radius of large ring domain according to three mechanismsObtaining the reference angle/>, generated by each grid point in the large ring domain under three mechanisms

And (5) obtaining the large-ring-domain lattice points to be matched under different mechanisms according to the steps (5) - (8).

Position coordinates of lattice points to be matched on inner-middle-outer ring layer of large ring domain under 1 sigma-EPMP mechanismThe calculation formula of (2) is as follows:

position coordinates of lattice points to be matched on inner-middle-outer ring layer of large ring domain under 2 sigma-EPMP mechanism The calculation formula of (2) is as follows:

position coordinates of lattice points to be matched on inner-middle-outer ring layer of large ring domain under 3 sigma-EPMP mechanism The calculation formula of (2) is as follows:

In the method for improving underwater navigation accuracy based on the grid topological structure iterative optimal ring domain point, the iterative calculation of the matching efficiency evaluation index of the large ring domain grid point to be matched, and the obtaining of the optimal matching position of the underwater vehicle track end point in the large ring domain according to the optimal principle comprise the following steps:

Determining the total number N _ξ of lattice points to be matched on an inner-middle-outer ring layer of a large ring domain under a xi mechanism;

taking the (r) to-be-matched grid point of the large ring domain under the (xi) mechanism as a to-be-estimated track end point and recording as a point Wherein r ε {1,2, …, N _ξ };

point to Point The corresponding position (x _r,y_r) in the gravity reference graph is compared with the grid resolution C of the gravity reference graph, and the nearest neighbor point/>, on the gravity reference graph, is obtained according to the rounding principleGrid point base _L of (a);

The gravity value corresponding to the grid point base _L As a dot/>Approximation of the gravity value;

Extracting and obtaining points according to the position coordinates of the grid point base _L on the gravity reference diagram and the navigation speed and the navigation direction of the underwater vehicle navigation Corresponding gravity graph track sequence/>Corresponding nearest neighbor gravity sequence

Will beComparing the measured gravity value sequence { g ₁,g₂,…,g_L } with the measured gravity value sequence { g ₁,g₂,…,g_L } of the underwater vehicle, calculating a matching efficiency evaluation index, and marking the matching efficiency evaluation index as MSD _r;

Sequentially calculating to obtain the matching efficiency evaluation indexes of all lattice points to be matched on the inner-middle-outer ring layer of the large ring domain under the zeta mechanism, and obtaining the matching efficiency evaluation index set and { MSD _r|r＝1,2,…,N_ζ };

Based on { MSD _r|r＝1,2,…,N_ζ }, screening and obtaining the best matching position of the underwater vehicle track end point in the large-loop domain range according to the optimal principle

Correspondingly, the invention also discloses a system for improving underwater navigation precision based on the iterative optimal ring domain point of the grid topological structure, which comprises the following steps:

the resolving module is used for obtaining the optimal matching position of the track starting point of the underwater vehicle through a small loop domain grid matching positioning strategy of the track starting point;

the generation module is used for generating lattice points to be matched of the large loop domain through a three-layer loop domain matching positioning strategy of the track end point changing angle according to the optimal matching position of the track start point of the underwater vehicle;

The iteration determining module is used for iteratively calculating the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and obtaining the optimal matching position of the underwater vehicle track end point in the large loop domain according to the optimal principle so as to realize the effective matching and positioning of the underwater vehicle track end point, further correct the control parameters of the INS system and assist in completing the navigation target of the underwater vehicle with long endurance and long range.

The invention has the following advantages:

In order to break through the limitation of the inherent grid structure of the traditional gravity matching algorithm and improve the underwater gravity matching navigation precision, the invention provides a novel grid topological structure iteration optimal ring domain point method (IOAP), which is based on the following principle: firstly, constructing a matching positioning strategy of a small-loop-domain grid of a track starting point according to the position of the inertial navigation starting point, drift errors, rotation angles and the like, and obtaining the optimal matching positioning of the track starting point and enhancing the insensitivity of an algorithm to the initial position errors through the matching comparison of the matching points of the small-loop-domain grid; secondly, constructing a matching positioning mechanism of three-layer loop domains with variable angles of the track end point by utilizing the optimal matching position of the track start point and combining inertial navigation course distance information, accumulated drift errors and the like so as to generate loop domain matching points of a loop topology structure; and finally, iteratively calculating the matching index of the loop matching points and obtaining the optimal matching position of the track endpoint in the loop range according to the optimal principle.

The statistical index of the matching precision, the average matching time, the matching success rate and the like are comprehensively considered as analysis bases of the matching quality, and the matching performance difference and the good robustness of the method under different inertial navigation accumulated error multiples or reference angle loop radiuses are verified.

The gravity matching test comparison is carried out on tracks of which the starting points and the ending points of different areas fall in different gravity intervals, so that the method proves that: the method has the advantages of high matching precision, strong positioning applicability of different gravity sections and the like, and the average matching precision and the worst matching precision of the method are respectively improved by 40.39 percent and 72.16 percent relative to the highest of a TERCOM algorithm.

Drawings

FIG. 1 is a flow chart of steps of a method for improving underwater navigation accuracy based on iterative optimal ring domain points of a grid topology in an embodiment of the invention;

FIG. 2 is a schematic diagram of distribution of small ring domain points to be matched based on SPMP strategy in an embodiment of the present invention;

FIG. 3 is a schematic diagram of distribution of points to be matched in a large ring domain based on EPMP strategies in an embodiment of the present invention;

FIG. 4 is a schematic illustration of an abnormal distribution of gravity in a satellite remote sensing and local amplification region of a research area according to an embodiment of the present invention; wherein, 4 (a) is a satellite remote sensing image, and 4 (b) is a gravity anomaly reference image;

FIG. 5 is a graph showing the comparison of the matching test effects of the gravity matching algorithm under different sigma criteria in an embodiment of the present invention; wherein 5 (a) is TERCOM algorithm, 5 (b) is 1σ -IOAP algorithm, 5 (c) is 2σ -IOAP algorithm, and 5 (d) is 3σ -IOAP algorithm;

FIG. 6 is a histogram of successful matching probabilities of 4 algorithms for different positioning accuracy in an embodiment of the invention;

FIG. 7 is a schematic diagram of a comparison of a matching position and a true position of a TERCOM algorithm and classification of lattice points in the embodiment of the invention; wherein 7 (a) is 100 tests of the TERCOM algorithm, and 7 (b) is the classification of the matching position points of the TERCOM algorithm;

FIG. 8 is a graph showing matching locations versus true locations for a different sigma-IOAP algorithm in accordance with an embodiment of the present invention; wherein 8 (a) is 100 tests of the 1 sigma-IOAP algorithm, 8 (b) is 100 tests of the 2 sigma-IOAP algorithm, and 8 (c) is 100 tests of the 3 sigma-IOAP algorithm;

FIG. 9 is a graph showing the comparison of the matching effect of IOAP algorithm under different reference angles of ring radius in an embodiment of the present invention; wherein 9 (a) is a TERCOM algorithm, 9 (b) is a 1-IOAP algorithm, 9 (c) is a 1.5-IOAP algorithm, 9 (d) is a 2-IOAP algorithm, and 9 (e) is a 2.5-IOAP algorithm;

FIG. 10 is a histogram of match success probabilities of IOAP algorithm at different ring radius reference angles in an embodiment of the present invention;

FIG. 11 is a schematic diagram showing a comparison of algorithm matching positioning effects under different track starting points in an embodiment of the present invention; wherein 11 (a) is a TERCOM algorithm (track start point a), 11 (B) is a TERCOM algorithm (track start point B), 11 (C) is a TERCOM algorithm (track start point C), 11 (d) is a 1.5-IOAP algorithm (track start point a), 11 (e) is a 1.5-IOAP algorithm (track start point B), and 11 (f) is a 1.5-IOAP algorithm (track start point C).

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention disclosed herein will be described in further detail with reference to the accompanying drawings.

The invention provides a method for improving underwater navigation accuracy based on a grid topological structure iteration optimal ring domain point, which is based on the structural layout of a TERCOM algorithm matching lattice and the drift error characteristic of an INS system, and is abbreviated as IOAP algorithm, and the implementation principle is as follows: in order to reduce the sensitivity of IOAP algorithm to initial error, taking inertial navigation starting point position as center and taking certain drift error and rotation angle Zhang Chengxiao ring domain matching lattice point, constructing a track starting point small ring domain matching positioning strategy of ring topology structure, and obtaining starting point optimal matching position according to the principle of minimizing absolute value of gravity deviation; according to the optimal matching position of the track starting point, the center position of a large-ring-domain matching grid is obtained by combining inertial navigation course distance information, the number of large-ring-domain matching grid rings is determined based on inertial navigation accumulated drift errors and the like, a topological structure of matching grid point annular distribution is obtained according to grid point reference deflection angles of middle ring radius and an inner-multiple-outer-half principle, a track end point variable angle three-layer ring domain matching positioning strategy is constructed, a gravity graph sequence is extracted through grid points, and then the mean square error is calculated and matched with an actual measurement gravity sequence, and compared, so that the optimal matching position of the track end point is obtained according to the principle of mean square error minimization.

As shown in fig. 1, in this embodiment, the method for improving underwater navigation accuracy based on iterative optimal ring domain points of a mesh topology includes:

and step 101, obtaining the optimal matching position of the track starting point of the underwater vehicle through a track starting point small-loop-domain grid matching positioning strategy.

In this embodiment, considering the problem of relative sensitivity of the gravity matching algorithm such as SITAN and ICCP to the initial error, and improving the insensitivity (robustness) of the IOAP algorithm to the gravity matching initial error to a certain extent, a track start small loop domain grid matching positioning strategy/mechanism is constructed (Matching Positioning strategy of THE TRACKING STARTING Point IN SMALL RING domain, SPMP). The SPMP strategy takes the inertial navigation indicating underwater vehicle position as the center, and opens into a small grid point area (marked as a small ring domain) of a small ring topology structure to be matched, which covers the true starting point position of the track according to probability, by taking a certain drift error and a certain rotation angle, and then the optimal matching position of the track starting point is determined according to the optimal principle of the gravity matching evaluation index of the point to be matched.

Preferably, the construction flow of the track starting point small loop domain grid matching positioning strategy can be as follows:

Acquiring an INS track output inertial navigation sequence { S ₁,S₂,…,S_L } of a certain section of underwater vehicle navigation according to a sampling time interval deltat; position coordinates and measured gravity values corresponding to the track points are extracted from { S ₁,S₂,…,S_L }, and a position coordinate sequence { (x ₁,y₁),(x₂,y₂),…,(x_L,y_L) } and a measured gravity value sequence { g ₁,g₂,…,g_L } of the underwater vehicle are constructed. Where L represents the sample sequence length of the INS track and S ₁、S₂、…、S_L represents each track point in the inertial navigation sequence.

Then, under SPMP strategy, the small loop is determined with the position coordinate (x ₁,y₁) of the track start point S ₁ in the inertial navigation sequence as the center and 3σ ₀ as the search boundary radius of the maximum extension. Further, according to the north-to-east rotation angle theta ₀ and the ring radius proportion average coefficient lambda, the total number and position coordinates of grid points to be matched in the small ring domain can be determined, and then the small ring domain grid point set to be matched is constructed. Where σ ₀ represents the standard deviation of the inertial navigation drift error at the sampling interval Δt. For example, when θ ₀ =45 and λ=1/3, the distribution of the small ring domain to-be-matched grid point set stretched by the SPMP strategy is shown in fig. 2.

Further, according to the setting condition of the parameter, the position coordinates of each lattice point to be matched in the small ring can be obtainedThe calculation formula of (2) is as follows:

Wherein, And/>Respectively representing the abscissa and the ordinate of the jth lattice point on the ith small ring; r _i represents the radius of the ith ring in the small ring domain, r _i＝3σ₀λi;β_j represents the rotation angle of the jth lattice point on each ring in the small ring domain, β _j＝j·θ₀; i=1, 2, …, and maximum value of i/>And maximum value of j/>

The determination procedure of the best matching position of the underwater vehicle track start point may be as follows:

the position coordinates (x ₁,y₁) of the track starting point S ₁ and the position coordinates of each lattice point to be matched in the small ring are calculated A point set to be matched, which is used as a true position of a track starting point; mapping a point set to be matched of the true position of the track starting point on a gravity reference map point by point, and obtaining a theoretical gravity value/>, corresponding to (x ₁,y₁), according to the gravity value at the nearest gravity reference grid point as the gravity value of the point to be matchedAnd/>Corresponding theoretical gravity value/>

In order to determine the optimal matching position of the starting point of the underwater vehicle track, the optimal matching position of the starting point of the underwater vehicle track can be obtained according to the principle of minimizing the absolute value of the gravity deviation

Wherein, C represents the grid resolution of the gravity reference graph, mapt (·, ·) represents the gravity value matrix of the gravity reference graph at the grid point positions, [ · ] represents rounding; when i=0 and j=0,Namely x ₁,/>I.e. y ₁.

It should be noted that, when the gravity reference map is mapped by the small-range multi-grid points, a multi-mode phenomenon may occur, in this embodiment, the matching is performed according to the minimum grid point of the first gravity deviation absolute value, and of course, the optimal matching of the track starting point may also be obtained according to a random selection mechanism, which is not limited in this embodiment.

From the above, the SPMP strategy can realize effective matching and positioning of the track starting point of the underwater vehicle according to probability, weaken the sensitivity of IOAP algorithm to initial errors, and provide a position information foundation of the track starting point for the next track end point position matching and positioning based on inertial navigation course distance information guidance.

Step 102, generating large-loop-domain lattice points to be matched through a three-layer loop-domain matching positioning strategy with an angle varying end point of the track according to the optimal matching position of the start point of the underwater vehicle track.

In the embodiment, considering that the inertial navigation track sequence is better in short-time high-precision course and course distance information, the track starting point best matching position obtained by combining SPMP strategiesThe center position O of the track end point domain to be matched can be further obtained; then constructing a new topological structure of grid Point annular distribution to be matched and a matching positioning strategy (track end Point variable angle three-layer annular domain matching positioning strategy/mechanism) based on drift error statistical characteristics of an inertial navigation system, wherein the EPMP strategy is used for accumulating relative relations among drift error standard deviation sigma, grid resolution C of a gravity reference diagram and radius of an intermediate ring by inertial navigation to obtain the maximum ring number of an annular coverage area and deflection angles among matching grid points, and expanding the maximum ring number and the deflection angles into a large variable angle three-layer topological structure annular grid Point area to be matched which covers the track real end Point position according to probability, and recording the maximum ring domain; and extracting a gravity graph sequence of the matched flight path according to the coordinate positions of the grid points, comparing the gravity graph sequence with a real gravity sequence of the underwater vehicle, and determining the optimal matching position of the flight path end point according to an evaluation index optimal principle.

Preferably, the construction flow of the track end point variable angle three-layer loop domain matching positioning strategy can be as follows:

Wherein d _INS represents the distance measurement between the start point and the end point of the inertial navigation track which is marked by h epsilon [0, + ] norm, and setting h=2, namely calculating Euclidean distance; alpha _INS represents a heading radian angle in a positive direction with the abscissa of the coordinate system.

Then according toAnd course information and range information of the underwater vehicle navigation, estimating and obtaining central position coordinates (x _O,y_O) of the large ring domain:

Then, under EPMP strategy, taking (x _O,y_O) as a center, taking R _max as the maximum extension searching boundary radius, constructing and obtaining the variable-angle three-layer topological structure annular grid point area covering the track real end point, and recording the variable-angle three-layer topological structure annular grid point area as a large annular domain. Meanwhile, the grid resolution C of the gravity reference diagram is used as the span interval of each ring of the large ring domain to obtain the total ring number of the large ring domain

On the basis, the deflection angle between adjacent grid points on each ring of the large ring domain is given, and a grid point set to be matched can be formed. If the same manner as SPMP strategy is selected to determine the deflection angle between adjacent lattice points on each ring of the large ring domain, the deflection angle of the large ring domain and the like can keep equal amount of lattice points of each ring, so that the lattice net stretched by SPMP strategy has the phenomenon of 'inner dense outer thin', namely the interval between lattice points of the inner ring is too small, and the interval between lattice points of the outer ring is too large. Therefore, for the large ring domain, the embodiment of the invention provides a novel mode for determining the deflection angle between adjacent lattice points, namely a variable-angle three-ring layer lattice point topological structure: grid resolution C and intermediate ring based on gravity reference diagramIs used as the reference angle/>, generated by each grid point in the large ring domainAccording to the principle of 'inner-multiple outer-half', determining the span angle between adjacent lattice points on the inner layer ring of the large ring domain as/>The span angle between adjacent lattice points on the outer layer ring is/>And combining the total number M of rings to construct a large-ring domain grid point set to be matched, and obtaining a variable-angle three-ring layer grid point topological structure. For example, when/>When m=9, the distribution of the large-loop domain to-be-matched grid point set stretched by EPMP strategies is shown in fig. 3.

In this embodiment, considering the good applicability of normal distribution to any systematic error under natural conditions and the high probability coverage characteristic of 3 sigma criterion 99.73%, and meanwhile, for the following test of the underwater gravity matching efficiency difference of EPMP strategies under the principle of different sigma (sigma, representing inertial navigation accumulated drift error standard deviation), three IOAP algorithms based on 1 sigma-EPMP, 2 sigma-EPMP and 3 sigma-EPMP mechanisms are respectively constructed according to the different sigma principles, which are abbreviated as 1 sigma-IOAP, 2 sigma-IOAP and 3 sigma-IOAP. Wherein, using ζ=1, 2,3 to represent three mechanisms of 1σ -EPMP, 2σ -EPMP, 3σ -EPMP, i.e., ζ=1, represents 1σ -EPMP mechanism; ζ=2, representing 2σ -EPMP mechanism; ζ=3, representing the 3σ -EPMP mechanism.

The EPMP policy can be constructed in the new manner as follows:

The total number of macrocycles under the three mechanisms 1 sigma-EPMP, 2 sigma-EPMP and 3 sigma-EPMP are respectively recorded as

Considering that the total number of the large ring domain is not necessarily 3 times under different sigma principles, and simultaneously, in order to further enhance the good coverage effect of EPMP strategy large ring domain lattice points on the actual position of the underwater vehicle, the method can be applied to the following formulaRespectively correcting to obtain the total number M ₁′、M₂′、M₃' of the large ring domain under three mechanisms after correction so as to facilitate the subsequent division of the inner, middle and outer three ring layers of the large ring domain:

Then, according to the corrected total number M ₁′、M₂′、M₃' of the large ring domain under three mechanisms, the radius R _1,j、R_2,j、R_3,j of each ring of the large ring domain under three mechanisms and the radius of the middle ring of the large ring domain under three mechanisms can be obtained

R_ξ,ζ＝ζC,ζ＝1,2,…,M′_ξ···(6)

Wherein R _ξ,ζ represents the radius of the ζ ring of the macrocyclic domain under the ζ mechanism.

Furthermore, the reference angle generated by each lattice point in the large ring domain under three mechanisms can be obtained

Finally, according to (5) - (8), the large-ring-domain lattice points to be matched under different mechanisms are obtained. The calculation formula of the large-loop domain lattice point to be matched under each mechanism is as follows:

position coordinates of lattice points to be matched on inner-middle-outer ring layer of large ring domain under 1 sigma-EPMP mechanism The calculation formula of (2) is as follows:

position coordinates of lattice points to be matched on inner-middle-outer ring layer of large ring domain under 3 sigma-EPMP mechanism The calculation formula of (2) is as follows: /(I)

And 103, iteratively calculating the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and obtaining the optimal matching position of the underwater vehicle track end point in the large loop domain according to the optimal principle so as to realize the effective matching and positioning of the underwater vehicle track end point, thereby correcting the control parameters of the INS system and assisting in completing the long-endurance long-range navigation target of the underwater vehicle.

In this embodiment, the gravity value calculated by interpolation method may not actually reflect the actual gravity at the matching point in consideration of the higher accuracy of the gravity value at the grid resolution in the gravity reference map, so a matching process similar to the conventional TERCOM algorithm may be adopted to determine the optimal matching location of the point to be matched in the EPMP large-loop region for concentrating the underwater vehicle endpoint.

Preferably, a feasible iterative calculation is performed on the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and the optimal matching position of the underwater vehicle track endpoint in the large loop domain is obtained according to the optimal principle in the following manner:

And determining the total number N _ξ of lattice points to be matched on an inner-middle-outer ring layer of the large ring domain under the zeta mechanism.

Taking the (r epsilon {1,2, …, N _ξ }) th lattice point to be matched of the large ring domain under the zeta mechanism as the end point of the track to be evaluated and recording as the pointWill be dot/>The corresponding position (x _r,y_r) in the gravity reference graph is compared with the grid resolution C of the gravity reference graph, and the nearest neighbor point/>, on the gravity reference graph, is obtained according to the rounding principleIs a grid point base _L of (c). Gravity value/>, corresponding to grid point base _L As a dot/>Approximation of the gravity value; extracting and obtaining a point/>, according to the position coordinate of the grid point base _L on the gravity reference diagram, the navigation speed and the navigation direction of the underwater vehicle navigationCorresponding gravity graph track sequence/>Corresponding nearest neighbor gravity sequence/>Will/>Comparing with the gravity value sequence { g ₁,g₂,…,g_L } actually measured by the underwater vehicle, and calculating a matching efficiency evaluation index which is recorded as MSD _r. In the embodiment, the mean square error MSD is taken as an example, and any appropriate performance evaluation index may be selected to perform calculation matching when calculating the matching performance evaluation index.

According to the calculation process of the (r) th lattice point to be matched, the matching efficiency evaluation indexes of each lattice point to be matched on the inner-middle-outer ring layer of the large ring domain under the (xi) th mechanism can be calculated in sequence, and the matching efficiency evaluation index set and { MSD _r|r＝1,2,…,N_ζ } of all lattice points to be matched on the inner-middle-outer ring layer of the large ring domain under the (xi) th mechanism are obtained.

Finally, based on { MSD _r|r＝1,2,…,N_ζ }, screening and obtaining the best matching position of the underwater vehicle track end point in the large-loop domain according to the optimal principle

In summary, through the steps 101 to 103, effective matching and positioning of the underwater vehicle end point are realized, and the obtained optimal matching position of the underwater vehicle track end point is used as the underwater vehicle track end point to correct the INS system control parameters and assist in completing the long-endurance long-range navigation target of the underwater vehicle.

On the basis of the embodiment, the method for improving the underwater navigation accuracy based on the iterative optimal ring domain point of the grid topology structure, which is described in the embodiment, is verified.

To verify the effectiveness and superiority of this IOAP algorithm in underwater submerged gravity navigation applications, a total of 3 sets of tests were designed:

Test 1, verifying the matching performance difference of IOAP algorithms under different sigma criteria;

test 2, verifying different influences of the reference angles under different ring radiuses on algorithm matching performance;

And 3, verifying good applicability of the proposed IOAP algorithm to underwater gravity matching navigation by using the track starting points of different areas.

Example data were derived from the san Diego division website (http:// topex. Ucsd.edu /) at the university of California, with 1'×1' resolution of gravity anomaly data. As shown in fig. 4 (a), the invention selects the gravity anomaly data of the south China sea area for research, wherein the longitude and latitude values of the data are in the range of (longitude 113 DEG E-115 DEG E, latitude 10 DEG N-12 DEG N). The present invention converts the gravity anomaly reference data into 100m×100m grid resolution gravity data by bilinear interpolation, and as shown in fig. 4 (b), the gravity anomaly in this region has a maximum value of 130.57mGal, a minimum value of-33.53 mGal, and an average value of 15.43mGal.

(1) Verification of matching performance variability of IOAP algorithm under different sigma criteria

The gravity anomaly grid resolution in the analog sample block is 100m multiplied by 100m, the accelerometer constant value zero bias is 10 ^-3m/s² (inertial navigation root mean square error is subject to normal distribution), the navigational speed is 10m/s ¹, the heading north is 70 degrees, the initial position error is 0m, the speed error is 0.04m/s ¹, the heading error is 0.05 degrees, the gravity meter real-time measurement data is random noise with the standard deviation of 1mGAL superimposed on the sampling value of the real track in the gravity anomaly database, the sampling point number is 110, and the sampling period is 20s. The invention defines the matching positioning precision as l, the difference between the absolute values of the matching position and the real position is effective matching in the closed interval [0,l ], thus the effective matching times N of the algorithm under the N times of experimental tests and the matching success rate of the algorithm can be obtainedAnd simultaneously recording the average value (mean), standard deviation (std) and worst value (max) of N times of test matching positioning accuracy and track average matching time T (without environment configuration time) as performance evaluation indexes of the gravity matching algorithm.

In order to test and analyze the application performance of IOAP algorithms with different sigma criteria in underwater vehicle gravity matching navigation, 100 independent experiments are carried out by using 1 sigma-IOAP, 2 sigma-IOAP and 3 sigma-IOAP algorithms, meanwhile, a TERCOM algorithm is used as a comparison algorithm, and gravity reference grid point coordinates (1400, 1500) are used as simulation starting points of underwater vehicle navigation, so that the visual matching positioning accuracy comparison effect is shown in fig. 5.

The matching positioning performance of IOAP algorithm under the action of different sigma is different, and the matching effect of 3 sigma-IOAP algorithm is most excellent and is obviously superior to that of the traditional TERCOM algorithm. On the T index, the 1 sigma-IOAP algorithm has the smallest average running time but has poor matching effect, so that the method is difficult to be effectively applied to the navigation of an actual underwater vehicle; the average running time of the 2σ -IOAP algorithm is about half of that of the TERCOM algorithm, the average matching precision is smaller than 1 grid resolution, and the matching probability is better than that of the TERCOM algorithm (88% > 82%) under the condition of the positioning precision l=100, which shows that the 2σ -IOAP algorithm has certain practical navigation application value under the double-target condition of the matching efficiency and the matching precision, and the gravity navigation mechanism can be adaptively selected according to the practical navigation requirement; the 3 sigma-IOAP algorithm is not much different from the T index of the TERCOM, and is superior to the TERCOM algorithm and most of index values of other algorithms in indexes such as mean value, std value, max value, matching success probability and the like of matching precision, so that the better matching performance and good potential practical value of the IOAP algorithm in underwater submerged power assisted navigation are fully shown.

To further analyze the difference of successful matching effects of the 4 algorithms under the constraint condition of different positioning accuracy L, the coefficients are respectively represented by L as 20, 40, 60, 80, 100 and 100A visual comparison histogram of the successful match probability versus outcome of 100 tests under the constraint of (a) is shown in fig. 6.

The successful matching probability difference of different sigma-IOAP algorithms under different positioning precision constraints is obvious, and 3 sigma-IOAP algorithms have successful matching for a certain number of times when l is less than or equal to 40, but TERCOM is matched and fails; the successful matching probability of the algorithm under different positioning accuracy I is comprehensively analyzed, the matching performance of the 3 sigma-IOAP algorithm is best, the 2 sigma-IOAP times is superior to the traditional TERCOM algorithm, and the positioning accuracy constraint of the 1 sigma-IOAP algorithm is weaker than that of the TERCOM; fig. 6 further intuitively shows the excellent successful matching performance of the 3σ -IOAP algorithm, and the conclusion still effectively verifies the excellent performance of the proposed 3σ -IOAP algorithm in underwater gravity matching navigation.

To further explore and analyze the reasons that the 3 sigma-IOAP algorithm is superior to the matching efficiency and accuracy of the TERCOM algorithm and other sigma-IOAP algorithms, a comparison diagram of the scattered points of the matching position of the TERCOM algorithm and the actual position of the underwater vehicle is shown in fig. 7 (the inertial navigation position is taken as the origin of coordinates of the image to ensure that all 100 test results can be drawn on the same image).

As can be seen from the analysis in fig. 7 (a), the actual positions of the underwater vehicle tested by the TERCOM algorithm 100 times are almost all located in the 3 sigma error grid range of the inertial navigation position (the solid line frame outside the dashed line circle in fig. 7 (a)), and all the position points are almost all located in the 3 sigma ring boundary dashed line inside the inertial navigation position (the dashed line circle inscribed with the solid line frame in fig. 7 (a)), that is, a certain amount of small probability matched points exist at the points to be matched of the rectangular grid of the TERCOM algorithm, see the points between the solid line frame and the dashed line circle in fig. 7 (b), that is, although the probability of being matched is smaller, the matching efficiency of the TERCOM is obviously affected, so that the gravity matching algorithm for deleting the matching points outside the 3 sigma ring can effectively improve the matching efficiency of the algorithm while not significantly affecting the matching accuracy of the algorithm, and effectively prove the feasibility and effectiveness of the lattice point matching mechanism based on the ring domain topology design of the invention from the side.

For further analysis of the reasons why the 3σ -IOAP algorithm is superior to the other σ -IOAP matching accuracy, a schematic of the matching effect of 100 tests of the different σ -IOAP algorithms is plotted as shown in fig. 8. From the analysis of fig. 8, the effect of the IOAP algorithm based on different sigma loop domains on the coverage matching of the actual position of the underwater vehicle is different: 3 sigma-IOAP algorithm realizes high-precision matching and positioning of the underwater vehicle with large-ring-domain lattice point coverage; the 2 sigma-IOAP and the 1 sigma-IOAP only realize good matching of the real position in the large ring, but the optimal matching of the real position outside the large ring is often only scattered on the boundary ring, and the better matching position is difficult to find, so that the matching precision of the algorithm is influenced, the difference of successful matching probabilities of different sigma-IOAP algorithms and the good matching precision of the 3 sigma-IOAP algorithm are explained to a certain extent, and the important potential application value of the 3 sigma-IOAP algorithm in underwater submerged gravity matching navigation is shown.

(2) Effect variability verification of IOAP algorithm matching performance under different ring radius reference angles

To further explore the reference angle based on different ring radii REffect on matching performance of 3 sigma-IOAP algorithm, the maximum ring radius R ¹＝R_M/3 of the inner ring layer and the middle ring radius of the inner middle ring layer of the 3 sigma-IOAP algorithm are respectively usedMaximum radius of intermediate ring R ²＝R_2M/3, "intermediate ring radius of outer ring layer/>To determine the reference angle/>, of the 3σ -IOAP algorithmAnd the lattice point sets to be matched of the variable-angle tricyclic layers are formed by stretching, and the corresponding algorithms are respectively recorded as 1-IOAP, 1.5-IOAP (namely 3 sigma-IOAP of section 2.1), 2-IOAP and 2.5-IOAP algorithms. From the analysis of equation (6), it can be seen that the larger the ring radius R, the larger the ring domain reference angle/>, of the 3σ -IOAP algorithmThe smaller the total number N of grid points to be matched is, the more the total number N of grid points to be matched is generated, so that the matching efficiency of the algorithm of 1-IOAP is the fastest and the execution of the algorithm of 2.5-IOAP is the slowest theoretically.

The parameter settings of the simulated sample block, the track starting grid coordinates and the like are the same as the parameter settings (1), and the algorithm matching positioning statistical results of 100 independent tests are respectively shown in fig. 9 and 10. The matching precision and success probability of the 3 sigma-IOAP algorithm almost show the characteristic of improving the matching effect along with the increase of the radius R of the reference angle ring, and the matching performance of the 3 sigma-IOAP algorithm is influenced by the reference angle based on different ring radii as shown by the fact that the matching effect of the 3 sigma-IOAP algorithm is relatively poor with the optimal performance of 2.5-IOAP and 2-IOAP times and is still better than that of the TERCTOM algorithm on the 6/7 index; according to the T index analysis, IOAP algorithm of different ring radius reference angles shows the phenomenon of 'reduced matching efficiency' along with the increase of the ring radius R, and the result is consistent with the theoretical analysis conclusion, which shows that the total amount of grid points to be matched corresponding to the different ring radius reference angles is different and the difference of algorithm matching efficiency is caused. Therefore, in the practical underwater vehicle navigation application, the 3 sigma-IOAP algorithm corresponding to the proper ring radius R can be selected with suitability according to specific matching target requirements and a specific navigation matching task is completed, so that the 3 sigma-IOAP algorithm provided by the invention has higher algorithm robustness and good application value in underwater gravity matching navigation.

2.5-IOAP algorithm matching precision and matching success probability under different scales are almost superior to other IOAP algorithms and TERCOM algorithms, but the matching T index value is highest and is almost 2 times of the TERCOM matching time, so that the 2.5-IOAP algorithm is not the best choice of gravity matching algorithm under the underwater navigation condition that the matching efficiency requirement is not high and the matching precision is more important; under the situation of double target requirements of matching efficiency and matching precision, the 1-IOAP algorithm is the best choice of the gravity matching algorithm, so that the 3 sigma-IOAP algorithm is further proved to have better underwater gravity matching navigation robustness. In addition, the 1.5-IOAP algorithm (i.e., 3 sigma-IOAP algorithm of section 2.1) shows a good compromise between matching efficiency and matching accuracy, while considering that its matching efficiency is not much different from that of the TERCOM algorithm, the following section further tests according to the 1.5-IOAP algorithm to verify its good matching performance at different starting points.

(3) Verification of good matching performance of IOAP algorithm under different area track starting points

To further verify the good matching applicability of the 1.5-IOAP algorithm in underwater vehicle matching navigation, the invention uses the regional grid coordinates a (1660, 1410), B (1550, 740) and C (1400, 350) as the positions of the underwater vehicle track starting points respectively. On the premise of little difference of matching efficiency (T index), the 1.5-IOAP algorithm is obviously superior to the traditional TERCOM algorithm on 4 indexes such as average matching precision (mean), standard deviation of matching precision (std), worst matching precision (max) and the like; compared with TERCOM, the worst matching precision of 1.5-IOAP under 3 situations is relatively improved by 47.24%, 63.96% and 72.16%, and the average matching precision is relatively improved by 20.37%, 40.39% and 13.88%, which shows that the IOAP algorithm provided by the invention has higher matching precision and good synchronous optimizing performance of multiple tests; meanwhile, under different successful matching scales, the algorithm of the invention has relatively more excellent matching success probability compared with the TERCOM algorithm, particularly, when the matching precision is smaller than 40, the algorithm of 1.5-IOAP still has certain probability to realize successful matching of the underwater vehicle position, and the good matching applicability of the proposed IOAP algorithm to underwater gravity matching navigation under different area starting points is effectively verified.

In order to intuitively show the difference of the matching effect of the 1.5-IOAP algorithm and the TERCOM algorithm under 3 test positions, the worst matching positioning comparison illustration in 100 tests is drawn, as shown in FIG. 11. As can be seen from the analysis of FIG. 11, in the underwater gravity matching positioning under the track start points of different areas, the 1.5-IOAP algorithm has higher matching positioning precision compared with the TERCOM algorithm, and meanwhile, the 3 track end points fall in different gravity interval sections, so that the better matching adaptability of the proposed IOAP algorithm in different gravity sections is effectively verified to a certain extent, and the effectiveness and the reliability of the iterative optimal ring domain point algorithm based on the novel grid topological structure in improving the underwater gravity matching precision are further effectively verified.

On the basis of the embodiment, the invention also discloses a system for improving underwater navigation precision based on iterative optimal ring domain points of a grid topological structure, which comprises the following steps: the resolving module is used for obtaining the optimal matching position of the track starting point of the underwater vehicle through a small loop domain grid matching positioning strategy of the track starting point; the generation module is used for generating lattice points to be matched of the large loop domain through a three-layer loop domain matching positioning strategy of the track end point changing angle according to the optimal matching position of the track start point of the underwater vehicle; the iteration determining module is used for iteratively calculating the matching efficiency evaluation index of the lattice points to be matched in the large loop domain, and obtaining the optimal matching position of the underwater vehicle track end point in the large loop domain according to the optimal principle so as to realize the effective matching and positioning of the underwater vehicle track end point, further correct the control parameters of the INS system and assist in completing the navigation target of the underwater vehicle with long endurance and long range.

For the system embodiment, since it corresponds to the method embodiment, the description is relatively simple, and the relevant points are referred to the description of the method embodiment section.

Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.

What is not described in detail in the present specification belongs to the known technology of those skilled in the art.

Claims

1. The method for improving the underwater navigation precision based on the grid topological structure iterative optimal ring domain point is characterized by comprising the following steps:

2. The method for improving underwater navigation precision based on grid topology iterative optimal ring domain points according to claim 1, wherein the construction flow of the track starting point small ring domain grid matching positioning strategy is as follows:

3. The method for improving underwater navigation precision based on grid topological structure iteration optimal ring domain points according to claim 1, wherein the position coordinates of each grid point to be matched in a small ring domainThe calculation formula of (2) is as follows:

4. The method for improving underwater navigation precision based on iterative optimal ring domain points of grid topology according to claim 3, wherein,

r_i＝3σ₀λi

β_j＝j·θ₀

Wherein i=1, 2, …, and the maximum value of iAnd maximum value of jλ∈(0,1]。

5. The method for improving underwater navigation precision based on grid topology iterative optimal ring domain points according to claim 4, wherein the obtaining the optimal matching position of the underwater vehicle track starting point through the track starting point small ring domain grid matching positioning strategy comprises the following steps:

Wherein, when i=0 and j=0,Namely x ₁,/>I.e. y ₁.

6. The method for improving underwater navigation precision based on grid topology iterative optimal ring domain points according to claim 5, which is characterized in that the construction flow of the three-layer ring domain matching positioning strategy of the track end point variable angle is as follows:

7. The method for improving underwater navigation precision based on grid topological structure iteration optimal ring domain points according to claim 6, wherein the generation mode of the large ring domain grid points to be matched is as follows:

R_ξ,ζ＝ζC,ζ＝1,2,…,M′_ξ···(6)

8. The method for improving underwater navigation precision based on iterative optimal ring domain points of grid topology as set forth in claim 7, wherein,

9. the method for improving underwater navigation precision based on grid topology iterative optimal ring domain points according to claim 8, wherein iteratively calculating matching efficiency evaluation indexes of the large ring domain grid points to be matched and obtaining an optimal matching position of an underwater vehicle track endpoint in a large ring domain according to an optimal principle comprises:

point to Point The corresponding position (x _r,y_r) in the gravity reference map is compared to the grid resolution C of the gravity reference map and rounded to the nearest point/>, on the gravity reference mapGrid point base _L of (a);

10. The system for improving the underwater navigation precision based on the grid topological structure iterative optimal ring domain point is characterized by comprising the following components: