Method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration

Abstract

The application belongs to the technical field of underwater navigation, and particularly relates to a method for improving matching efficiency and reliability of underwater navigation based on domain center adaptive migration. The DAMM model of the technical scheme of the application is inspired by the ring contour distribution characteristics of the TERCOM matching domain internal evaluation index value, the nearest neighbor grid point coordinates of the end point of the inertial navigation indicated track are extracted on the gravity reference map, two initial matching grid point lines are generated along the transverse-longitudinal direction of the coordinates, and the best matching point position on the line is determined according to the index optimization principle; the matching domain center is adaptively migrated from the inertial navigation indicated nearest neighbor position to the best position on the transverse-longitudinal line, and the domain half side length is determined according to the size difference of the matching index value and is expanded to form a grid matching domain; and then the best matching position in the grid domain is obtained according to the matching index optimization principle. The DAMM model can effectively improve the matching efficiency of underwater gravity matching navigation and the reliability of out-of-domain positioning, and shows good applicability in different gravity track areas.

Description

Technical Field

[0001] This invention belongs to the field of underwater navigation technology, specifically a method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration. Background Technology

[0002] Underwater navigation and positioning technology is crucial for ensuring long-duration, high-precision underwater navigation of submersibles. Inertial navigation systems (INS), as a common, highly stealthy, all-weather, and autonomous passive navigation technology, suffer from performance limitations in underwater navigation applications due to the cumulative divergence of inertial navigation errors over time. Gravity field data, an inherent property of the Earth, is less affected by uncertain and complex environments such as climate and ocean waves. It is more stable than underwater topography and the geomagnetic field, exhibiting long-term relative invariance. As a reliable data source, it has been successfully applied in the field of underwater gravity-assisted INS navigation.

[0003] Gravity matching algorithms are a crucial component of gravity-assisted INS navigation technology, and their matching performance directly determines the accuracy, efficiency, and reliability of underwater navigation and positioning. Currently, common gravity matching algorithms mainly include Terrain Contour Matching (TERCOM), Iterative Nearest Contour Point (ICCP), and filtering algorithms. Among these, TERCOM, as a sequence matching algorithm, has received continuous attention and research due to its advantages such as insensitivity to initial errors and better matching robustness.

[0004] Due to issues such as limited matching domain features or spurious peaks in evaluation metrics, TERCOM often experiences large positioning errors where the optimal matching position within its domain is relatively far from the actual position of the submersible, i.e., mismatches (within the domain). This negatively impacts the calibration effectiveness of the INS system parameters and its actual navigation performance. Furthermore, its optimal matching positioning is determined through grid-by-grid searching, evaluation, comparison, and iterative updates within the matching domain, resulting in a relatively long matching time and demonstrating inefficiency. Therefore, improving its positioning reliability and matching efficiency are two important issues in TERCOM research.

[0005] Regarding research on TERCOM mismatches, Wang et al., using underwater terrain matching as an example, systematically studied the impact of terrain accuracy, map resolution, and initial inertial navigation error on TERCOM matching errors and provided confirmatory conclusions on TERCOM mismatches; they mentioned that due to terrain similarity, TERCOM is prone to mismatches in different neighboring areas. Wang et al., using underwater geomagnetic matching as an example, believed that excessive initial inertial navigation error and insufficient background features are the two main reasons for TERCOM mismatches, and proposed a similarity extreme value detection method for TERCOM mismatch diagnosis to improve TERCOM matching performance. Han et al. pointed out that the high resolution of the reference map and the uncertainty of gravity anomaly distribution can lead to TERCOM mismatches. They proposed a mismatch diagnosis method based on image registration (constrained spatial order constraint algorithm) by combining spatial order constraints and decision criterion constraints to improve TERCOM mismatch screening and matching accuracy. Dai et al. pointed out that when the matching region has smooth features, the mismatch probability of matching algorithms such as TERCOM and ICCP is relatively high. They proposed a real-time triple-constraint mismatch detection method based on reference data navigation by selecting a fitting model for the submersible navigation characteristics, detecting mismatches through affine transformations between tracks, and constraining track distance ratios. This method aims to effectively detect mismatch points and improve the matching reliability of the algorithm. Wang et al. mentioned that TERCOM experiences increased false peaks and mismatch probabilities due to the increase in the initial matching region, and the positioning results are highly unstable. Wang et al. pointed out that the COR index can lead to TERCOM mismatches to some extent, while MSD (Mean Squared Difference) is an effective matching index for determining the most relevant position, and its accuracy is slightly higher than MAD (Mean Absolute Difference) and COR (Cross Correlation). Wang et al. pointed out that in areas with low terrain adaptability, the TERCOM likelihood function is easily affected by measurement errors, resulting in false peaks and mismatches. Larger initial positioning errors also increase the number of false peaks and mismatches. They proposed a particle filter initialization method based on nonlinear multi-terrain assisted fusion positioning to improve positioning stability and accuracy.

[0006] Regarding the research on TERCOM matching efficiency, Shuai et al. pointed out that in complex terrain areas, the terrain entropy is low and there are fewer highly correlated sequences, which leads to low TERCOM matching accuracy; while in flat terrain areas, the terrain entropy is high and there are more highly correlated sequences, which leads to low TERCOM positioning efficiency. Therefore, they proposed an underwater positioning dynamic TERCOM algorithm based on the dynamic change of matching sequences to improve its matching accuracy and positioning efficiency.

[0007] The aforementioned studies primarily focus on TERCOM mismatches or matching efficiency, particularly those within the TERCOM domain. However, due to the drift of the INS indicating the end point of the track and the limited boundaries of the TERCOM matching domain, the actual position of the underwater vehicle may lie outside its effective matching domain and not covered by all grid points within the domain, leading to significant positioning errors due to mismatches outside the domain. Currently, research on TERCOM mismatches outside the domain is relatively limited. This invention has conducted preliminary research and proposed the SLSR model. Test results show that the out-of-domain regeneration matching mechanism at the optimal boundary matching point can effectively avoid out-of-domain mismatches and improve positioning accuracy; simultaneously, adjusting the size of the initial matching domain can improve the matching efficiency of the algorithm to some extent. However, the SLSR model's initial matching domain and out-of-domain regeneration matching domain both obtain the optimal matching position of the underwater vehicle through traversal search and matching comparison of all grid points within the domain, which still limits the potential for improving matching efficiency. Therefore, further in-depth research is needed on improving the matching efficiency of underwater gravity matching navigation and the mechanism for avoiding out-of-domain mismatches. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention proposes a method for improving the matching efficiency and reliability of underwater navigation based on domain center adaptive migration, with the goal of improving the matching efficiency of underwater gravity matching navigation and the auxiliary goal of improving the performance of avoiding mismatches outside the domain.

[0009] This invention relates to a method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration, comprising the following steps:

[0010] S1, using the position indication output by the inertial navigation system at the end of the underwater vehicle's journey. Determine the center position of the pre-matching line

[0011] S2, based on the lateral and longitudinal inertial navigation drift errors of the underwater vehicle, calculate the number of half-grid points on the two pre-matching lines in the lateral and longitudinal directions, respectively, n. row and n co l;

[0012] S3, based on the optimal matching position information on the horizontal pre-matching line and the optimal matching information on the vertical pre-matching line, and according to the principle of minimizing the index value, compare... and The relative size of the values ​​is used, and the best online position corresponding to the best matching index value and the matching index value are respectively used as the center position of the new migration domain. and matching index value

[0013] S4, based on the adaptive new matching domain center position and the number of grid points for the horizontal and vertical half-side lengths and Determine the coordinates of each grid point within the domain and determine the optimal matching position within the new matching domain based on the principle of optimal matching index value.

[0014] S5. Compare and obtain the best matching position within the domain according to the principle of minimizing the index. This position is used to correct the corresponding end point of the inertial navigation system's trajectory, thereby calibrating the parameters of the corresponding sensors within the inertial navigation system and assisting in completing the high-precision underwater navigation mission of the underwater vehicle.

[0015] Preferably, in step S1, the center position of the pre-matching line That is, the inertial navigation system indicates the endpoint position. Coordinates of the nearest neighbor grid point on the gravity reference map Its calculation expression is,

[0016]

[0017] Where C represents the grid resolution of the gravity reference map.

[0018] Preferably, in step S2, the calculation formula is expressed as follows:

[0019]

[0020] Where, σ x and σ y These represent the lateral and longitudinal cumulative drift errors of the INS system, respectively.

[0021] Preferably, in step S3, the grid point positions on the horizontal pre-matching line are... As the endpoint of the reverse-order track, the nearest neighbor grid point of each track point on the gravity map is deduced by combining navigation information, and the gravity value of the grid point is extracted as the alternative gravity value of the track point.

[0022] Preferably, the grid points on the horizontal pre-matching line are obtained by reversing the order. The sequence of gravity substitution values ​​corresponding to the end point of the track Recalculate The measured gravity value sequence G={g i Matching index values ​​between} Its calculation expression is,

[0023]

[0024] in, Represents the measured gravity sequence g i The inverse vector.

[0025] Preferably, the corresponding matching index values ​​of all grid points on the horizontal pre-matching line are obtained. Then, determine the optimal matching position on the horizontal line according to the principle of minimizing the matching index. Its calculation expression is,

[0026]

[0027] Simultaneously, the optimal horizontal index value corresponding to this optimal horizontal matching position and the position index in the horizontal grid vector are... and

[0028] Preferably, in step S3, the calculation expression is:

[0029]

[0030] Preferably, in step S4, an adaptive domain matching process is performed based on the domain center position. Matching index value The value of the control factor λ is adaptively determined by the magnitude of the factor, and its calculation expression is as follows:

[0031]

[0032] Where, Θ MSD This represents the critical threshold value of the matching index used to regulate the multiplier λ.

[0033] Preferably, in step S4, the horizontal grid point values ​​are... by Starting with and To terminate, the vertical grid point values ​​are... by Starting with and To terminate, then with For the core of the domain and with and The grid point positions within the domain are obtained by counting the number of grid points for each half-length of the horizontal and vertical axes. The calculation expression is,

[0034]

[0035] This invention takes improving the matching efficiency of underwater gravity-based navigation as its primary research objective and enhancing the performance of avoiding mismatches outside the domain as its secondary objective. It proposes a novel Domain-center Adaptive-transfer Matching Method (DAMM) to achieve a dual improvement in underwater navigation matching efficiency and outside-domain positioning reliability. The beneficial effects of this invention's technical solution are as follows: The DAMM model is inspired by the annular contour line distribution characteristics of evaluation index values ​​within the TERCOM matching domain. It extracts the coordinates of the nearest neighbor grid point of the inertial navigation indicated track endpoint on the gravity reference map, generates two initial matching grid lines along these coordinates in the horizontal and vertical directions, and determines the optimal matching point position on these lines according to the principle of optimal index. The matching domain center (domain center) adaptively migrates from the nearest neighbor position indicated by the inertial navigation to the optimal position on the horizontal and vertical lines, and determines the domain half-side length based on the difference in the magnitude of its matching index values, thus spanning a grid matching domain. Finally, the optimal matching position within this grid domain is obtained according to the principle of optimal matching index. The DAMM model can effectively improve the matching efficiency of underwater gravity matching navigation and the reliability of out-of-domain positioning, and shows good applicability in different gravity track areas. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of underwater gravity matching using the TERCOM algorithm of this invention;

[0038] Figure 2 This is a schematic diagram of mismatches within and outside the TERCOM domain under the matching index value of the present invention, wherein (a) mismatches within the domain and (b) mismatches outside the domain;

[0039] Figure 3 The selected simulation block contains a gravity baseline map (a) and a water depth map (b).

[0040] Figure 4 This is a visual comparative statistical diagram of the out-of-domain mismatch test, including (a) a line graph comparing positioning accuracy, (b) a frequency distribution graph of positioning accuracy, and (c) a comparison graph of the best match scatter.

[0041] Figure 5 This is a schematic diagram of the efficient localization of the adaptive matching domain of DAMM for in-domain matching tests, where (a) is the 0.5σ matching domain, (b) is the 1σ matching domain, and (c) is the 1.5σ matching domain.

[0042] Figure 6 This is a schematic diagram illustrating the effective and high-reliability localization of DAMM for out-of-domain matching tests. (a) 1σ matching domain; (b) 2σ matching domain; (c) 3σ matching domain;

[0043] Figure 7 A comparative statistical diagram of out-of-domain mismatch tests under different gravity ranges, including (a1) positioning accuracy curve of track A, (a2) frequency distribution of positioning accuracy of track A, (a3) ​​scatter plot of best matching position of track A, (b1) positioning accuracy curve of track B, (b2) frequency distribution of positioning accuracy of track B, (b3) scatter plot of best matching position of track B, (c1) positioning accuracy curve of track C, (c2) frequency distribution of positioning accuracy of track C, and (c3) scatter plot of best matching position of track C. Detailed Implementation

[0044] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] This invention relates to a method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration, comprising the following steps:

[0047] S1, using the position indication output by the inertial navigation system at the end of the underwater vehicle's journey. Determine the center position of the pre-matching line

[0048] S2, based on the lateral and longitudinal inertial navigation drift errors of the underwater vehicle, calculate the number of half-grid points on the two pre-matching lines in the lateral and longitudinal directions, respectively, n. row and n col ;

[0049] S3, based on the optimal matching position information on the horizontal pre-matching line and the optimal matching information on the vertical pre-matching line, and according to the principle of minimizing the index value, compare... and The relative size of the values ​​is used, and the best online position corresponding to the best matching index value and the matching index value are respectively used as the center position of the new migration domain. and matching index value

[0050] S4, based on the adaptive new matching domain center position and the number of grid points for the horizontal and vertical half-side lengths and Determine the coordinates of each grid point within the domain and determine the optimal matching position within the new matching domain based on the principle of optimal matching index value.

[0051] S5. Compare and obtain the best matching position within the domain according to the principle of minimizing the index. This position is used to correct the corresponding end point of the inertial navigation system's trajectory, thereby calibrating the parameters of the corresponding sensors within the inertial navigation system and assisting in completing the high-precision underwater navigation mission of the underwater vehicle.

[0052] like Figure 1 The underwater gravity matching mechanism of TERCOM is described in the figure. TERCOM, as an effective underwater gravity matching navigation algorithm, was first used to solve the terrain altitude matching problem for cruise missiles. Essentially, it is a sequence matching algorithm with good matching robustness, and its matching performance is unaffected by the initial position under conditions of a large matching domain and sufficient sampling. Typically, TERCOM uses a grid of points with a matching domain of 3 times the inertial navigation error as half the side length to ensure both good positioning accuracy and relatively high matching efficiency.

[0053] For ease of mathematical description, let L be the trajectory of the underwater vehicle traveling at speed v along a north-northeast direction θ, and let the sampling position sequence and the measured gravity value sequence be denoted by time interval Δt and sampling scale N, respectively. and G=g i , where i∈{1,2,L,N} represents the i-th sampling point. When i=N, the position of the indicated end point of the INS system output is denoted as .

[0054] On the gravity reference chart, the endpoint of the track is indicated by the INS. Based on the nearest neighbor principle, the coordinates of the center grid point of the TERCOM matching grid domain are determined as follows: Its calculation formula is:

[0055]

[0056] Where C represents the grid resolution of the gravity reference map, and [·] represents the rounding mode. Then, the number of horizontal and vertical grids on a single side of the TERCOM matching domain with a half-side length of 3 times the inertial navigation error (3σ) are n respectively. row and n col The corresponding calculation formula is:

[0057]

[0058] Where, σx and σ y These represent the lateral and longitudinal cumulative drift errors of the INS system, respectively. This indicates the round-up mode. Then TERCOM matches the grid point positions within the matching domain. The calculation formula is

[0059]

[0060] Where, k row =-n row :-n row +1:L:n row k col =-n col :-n col +1:L:n col .

[0061] Based on the underwater vehicle's speed v and heading θ, using the grid points within the domain The position sequence of the entire track to be matched is obtained by reversing the process from the endpoint of the track to be matched. Its calculation formula is:

[0062]

[0063] Where i = 1, 2, ..., N, ε v and ε θ Let represent the errors in speed v and heading θ, respectively. The sequence R is determined according to the grid nearest neighbor principle. k Each location point Find the coordinates of the nearest neighbor grid point on the gravity map M and extract its gravity value as an approximate replacement gravity value at that location. Its calculation formula is:

[0064]

[0065] From equation (5), we can obtain the lattice points within the TERCOM domain. The corresponding approximate alternative gravity sequence A k Taking the matching metric MSD as an example, then A k MSD between the measured gravity value sequence G and the actual gravity value sequence G k The calculation formula is

[0066]

[0067] in, Represents the measured gravity sequence g i The inverse vector. Note: k = 1, 2, ..., (2k... row +1)×(2k col+1) indicates that the position index of all grid points within the TERCOM matching field is the bottom left grid point, and the process is performed row-wise. MSD k grid points within the TERCOM domain It is a one-to-one correspondence.

[0068] To determine the optimal matching location (x) of grid points within the TERCOM domain opt ,y opt According to the principle of minimizing the MSD index, the corresponding calculation formula is as follows:

[0069]

[0070] As analyzed above regarding the TERCOM matching mechanism, TERCOM requires traversing and matching all grid points within the matching domain determined by the INS endpoint. This involves operations such as reverse tracking, extracting gravity values ​​from substitute grid points, and iteratively calculating matching index values ​​to ultimately obtain the optimal matching position. This allows for the calibration and correction of INS system control parameters and assists in underwater gravity matching navigation. However, this traversal matching process is relatively time-consuming, severely impacting the efficiency of the TERCOM algorithm's underwater gravity matching navigation and demonstrating the algorithm's inefficiency and poor real-time performance in assisting submersible navigation. This is essentially due to the indiscriminate traversal matching comparison of all grid points within the TERCOM domain. Therefore, a certain guidance mechanism can be used to regulate the adaptive grid generation of the matching domain and the class-discrepancy matching of grid points to improve the matching efficiency of underwater gravity matching navigation.

[0071] False matching occurs when the optimal matching position of the matching algorithm is more than a threshold away from the actual position of the underwater vehicle. The flatness of the navigation area or the spurious peaks in the matching index often lead to false matching within the TERCOM domain. Figure 2 As shown in (a); in addition, statistical analysis of previous tests revealed another type of mismatch in TERCOM, namely, the boundary limitations of the TERCOM matching domain cause the optimal matching position within the domain to fail to effectively approximate the actual position of the submersible, resulting in a large positioning error. Figure 2 As shown in (b). To facilitate the distinction between the two types of mismatches, based on the relative positional relationship between the optimal matching position and the actual position of the submersible, the former is an intra-domain mismatch, and the latter is an extra-domain mismatch.

[0072] Depend on Figure 2Analysis reveals that the TERCOM matching index values ​​generally exhibit a ring-shaped contour distribution. The positioning error of mismatches within the domain typically does not exceed a few grid resolutions, while the positioning error of mismatches outside the domain can reach tens of grid resolutions. From the perspective of the contour ring center, when a mismatch occurs within the domain, the ring center is located inside the TERCOM matching domain and relatively close to the optimal matching position (near the ring center); when a mismatch occurs outside the domain, the ring center is likely located outside the TERCOM matching domain and relatively far from the optimal matching position. Therefore, by leveraging the ring-shaped contour distribution characteristics of the matching index values, a certain number of grid points can be pre-matched to filter and adaptively generate matching grid domains, aiming to improve the matching efficiency and outside-domain positioning reliability of underwater gravity matching navigation.

[0073] The simulated track area was selected from a sea area within the latitude and longitude range (114°E–116°E, 9°N–11°N). To enable rapid simulation testing within the selected area, the original gravity reference map was interpolated using bilinear interpolation and proportionally scaled down to a 100m × 100m gravity map, as shown below. Figure 3 As shown in (a), the interpolated gravity map has a grid number of 2221×2221. The interpolated grid resolution of 100m×100m provides a good platform for the simulation analysis of matched navigation. Furthermore, to ensure the safe navigation of the underwater vehicle, no sampling point on the simulated track should be less than the water depth threshold. This invention sets the water depth threshold to 200m, meaning that areas with a water depth less than 200m are considered non-adaptive navigation zones under water depth constraints, and the simulated track must not cross these non-adaptive zones. Figure 3 (b) is a 3D schematic diagram of the water depth within the simulated area. In the simulation experiment, the gravity sensor sampling period of the underwater vehicle was 20s, and the number of sampling points on the track was 120.

[0074] To ensure the fairness and validity of the experimental results, each experimental group independently conducted 10,000 tests. The critical threshold Θ of the DAMM algorithm was used. MSD The value was set to 0.05, and the traditional TERCOM algorithm (3σ) was used as the comparison algorithm. The mean (Ave), standard deviation (STD), worst value, and best value of positioning accuracy from 10,000 tests, along with the average positioning time (T), were used as performance evaluation indicators for underwater gravity-matching navigation. Furthermore, the value was set to no more than the diagonal length of a unit grid (i.e.,...). Let Θ be the threshold for determining a valid match. The matching success rate ρ represents the ratio of valid matches to the total number of tests. If the positioning accuracy of the test exceeds this threshold, it indicates a false match occurred in that test. Simultaneously, to regulate and ensure the high matching efficiency of the DAMM algorithm, when Θ... MSD When ∈(0.25,0.35], the multiplier λ=2.5.

[0075] The experimental platform was a laptop computer with Windows 10 operating system and Intel(R) Core(TM) I7-8565U CPU. All code was implemented using Matlab2018a software.

[0076] To further visually demonstrate the localization performance of DAMM in the TERCOM out-of-domain mismatch test, we have plotted a line graph comparing localization accuracy, a frequency distribution graph, and a scattering graph comparing the best matching positions, as shown below. Figure 4 As shown.

[0077] Depend on Figure 4 Analysis shows that the successive positioning accuracy of DAMM is almost always significantly better than that of TERCOM's out-of-domain positioning error, and almost none of them exceed the critical threshold of mismatch. This indicates that the adaptive grid domain of the proposed DAMM can effectively cover the actual position of the submersible and achieve high-precision positioning for underwater navigation. Furthermore, considering the frequency distribution of positioning accuracy, most of DAMM's positioning accuracy is approximately half a grid resolution, while TERCOM's accuracy mostly exceeds three grid resolutions, and some even exceed ten grid resolutions. This indicates that the proposed DAMM can achieve high positioning reliability compared to TERCOM's out-of-domain mismatches. DAMM's optimal matching positions are clustered within a small neighborhood of the submersible's actual position, while TERCOM's optimal matching positions are scattered over a larger area around the actual position, some even far from the actual position, resulting in larger positioning errors. This verifies that the proposed DAMM algorithm has a relatively stronger ability to reposition and match submersible positions with large out-of-domain errors, thus calibrating the sensor control parameters of the INS system and assisting in underwater matching navigation.

[0078] To further explore the reasons for the high efficiency and good out-of-domain positioning reliability of the DAMM algorithm compared to TERCOM matching, examples of efficient positioning in intra-domain matching tests and effective and highly reliable positioning in out-of-domain matching tests using the DAMM adaptive grid domain are illustrated, as shown below. Figure 5 and Figure 6 As shown.

[0079] Depend on Figure 5 Analysis shows that the DAMM algorithm can adaptively migrate the domain center to its optimal position on the pre-matching grid points in the horizontal and vertical directions by optimizing the index values. It then generates matching grid domains with varying half-side lengths based on these index values, achieving rapid and high-precision positioning of the underwater vehicle's true location with relatively fewer grid points. In contrast, the TERCOM algorithm's matching efficiency is limited by the generation of a fixed, large-scale matching domain and the indiscriminate traversal search of grid points within the domain. The results demonstrate that the DAMM algorithm proposed in this invention, with its adaptive generation and re-matching of the matching domain guided by index values, can achieve high positioning accuracy in underwater gravity-based matching navigation while maintaining high efficiency in matching positioning.

[0080] Depend on Figure 6 Analysis shows that the DAMM algorithm can break the boundary limitations of the traditional TERCOM matching domain. It generates an adaptive grid domain outside the TERCOM domain by expanding the optimal index value of the endpoint domain center of the pre-matching line, and achieves high-precision positioning of the actual location of the submersible outside the domain based on probability. The smaller the MSD value of the domain center, the more effectively the small half-side grid domain can cover the actual location of the submersible outside the domain; conversely, a larger half-side length, or even the same half-side length as the TERCOM, is used to expand an adaptive matching domain with the endpoint as the domain center, maximizing the positioning performance of the actual location of the submersible outside the domain. Results show that the DAMM algorithm proposed in this invention, guided by the endpoint domain center index value, can adaptively generate a matching domain across the TERCOM boundary and its optimal matching positioning can achieve effective and highly reliable positioning of the actual location of the submersible outside the domain.

[0081] The above conclusions verify the high underwater gravity matching navigation efficiency, high reliability of out-of-domain mismatch positioning, and excellent matching accuracy of the proposed DAMM algorithm.

[0082] To further demonstrate the superior out-of-domain positioning performance of DAMM, a comparative diagram is shown below. Figure 7 As shown.

[0083] Depend on Figure 7 Analysis shows that the matching performance of the DAMM algorithm varies under different gravity ranges, but it is superior to the TERCOM algorithm in all cases. The DAMM algorithm can effectively improve the positioning accuracy of TERCOM out-of-domain mismatches in almost all cases, and it can even achieve effective matching for large errors exceeding 15 grid resolutions. From the perspective of the frequency distribution of positioning accuracy, the positioning accuracy of DAMM under the three gravity tracks almost never exceeds the mismatch judgment threshold, and the positioning accuracy is sometimes only about half a grid resolution. Regarding the true position of the submersible in TERCOM out-of-domain mismatches, the optimal matching position of DAMM almost always effectively approximates its small neighborhood and is significantly better than the widely dispersed TERCOM matching positioning, indicating that the proposed DAMM algorithm exhibits good out-of-domain positioning reliability under different gravity ranges.

[0084] The above results effectively verify that the DAMM algorithm of the present invention can effectively improve the underwater gravity matching navigation performance of the TERCOM algorithm, and has faster matching efficiency, higher reliability of out-of-domain positioning, and better positioning accuracy and matching success rate.

[0085] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the embodiments of the invention. Therefore, the embodiments of the invention are not to be limited to the embodiments shown herein, but are to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0086] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration, characterized in that, Includes the following steps: S1, using the position indication output by the inertial navigation system at the end of the underwater vehicle's journey. Determine the center position of the pre-matching line ; S2, based on the lateral and longitudinal inertial navigation drift errors of the underwater vehicle, calculate the number of half-grid points on the two pre-matching lines in the lateral and longitudinal directions respectively. and ; S3, based on the optimal matching position information on the horizontal pre-matching line and the optimal matching information on the vertical pre-matching line, and according to the principle of minimizing the index value, compare... and The relative size of the values ​​is used, and the best online position corresponding to the best matching index value and the matching index value are respectively used as the center position of the new migration domain. and matching index value ; S4, based on the adaptive new matching domain center position and the number of grid points for the horizontal and vertical half-side lengths and Determine the coordinates of each grid point within the domain and determine the optimal matching position within the new matching domain based on the principle of optimal matching index value. ; S5. Compare and obtain the best matching position within the domain according to the principle of minimizing the index. This position is used to correct the corresponding end point of the inertial navigation system's trajectory, thereby calibrating the parameters of the corresponding sensors within the inertial navigation system and assisting in completing the high-precision underwater navigation mission of the underwater vehicle.

2. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S1, the center position of the pre-matching line That is, the inertial navigation system indicates the endpoint position. Coordinates of the nearest neighbor grid point on the gravity reference map Its calculation expression is, in, This indicates the grid resolution of the gravity reference map.

3. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S2, the calculation formula is expressed as follows: in, and These represent the lateral and longitudinal cumulative drift errors of the INS system, respectively.

4. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S3, the grid point positions on the horizontal pre-matching line As the endpoint of the reverse-order track, the nearest neighbor grid point of each track point on the gravity map is deduced by combining navigation information, and the gravity value of the grid point is extracted as the alternative gravity value of the track point.

5. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S3, the calculation expression is: 。 6. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S4, an adaptive domain matching process is performed based on the domain center position. Matching index value Size adaptively determines the control factor The value of is calculated using the following expression: in, Indicates the use of multiplier factor for regulation The critical threshold for the matching index value.

7. The method for improving underwater navigation matching efficiency and reliability based on domain center adaptive migration according to claim 1, characterized in that, In step S4, the horizontal grid point values ​​are taken as follows. by Starting with and To terminate, the vertical grid point values ​​are... by Starting with and To terminate, then with For the core of the domain and with and The grid point positions within the domain are obtained by counting the number of grid points along the horizontal and vertical half-lengths. The calculation expression is: 。

Images