Underwater terrain matching suitability evaluation method based on terrain complexity
Through a terrain matching adaptability evaluation method based on terrain complexity, the problem of difficult to quantify the relationship between terrain complexity and terrain matching adaptability in the prior art is solved, and the terrain matching navigation accuracy of underwater vehicles is improved.
Patent Information
- Application Number
- CN202510006899.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-03
- Publication Date
- 2025-03-18
AI Technical Summary
In the prior art, the relationship between terrain complexity and terrain matching adaptability is difficult to quantify, resulting in low terrain matching navigation accuracy of underwater vehicles.
A terrain matching adaptability evaluation method based on terrain complexity is provided. By obtaining the terrain data of the target area, calculating the terrain feature parameters, calculating the terrain complexity, and constructing a terrain matching simulation test, establishing the relationship between regional terrain complexity and terrain matching adaptability, and quantitatively evaluating the adaptability of underwater terrain.
Through this method, the accuracy of terrain matching navigation of underwater vehicles can be improved, and the ability of AUV to carry out terrain matching and positioning based on the terrain complexity can be expanded.
Smart Images
Figure CN119642827A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of underwater navigation technology, and in particular to an underwater terrain matching adaptability evaluation method based on terrain complexity. Background Art
[0002] An autonomous underwater vehicle (AUV) is a robot that can operate independently underwater without being physically connected to the water surface. It is used to perform various underwater tasks, such as marine scientific research, environmental monitoring, resource exploration, and military applications. Terrain matching navigation is an important means to improve the positioning accuracy of AUVs. The complexity of the seabed terrain directly affects the accuracy of terrain matching navigation. The accuracy of terrain matching navigation is affected by acoustic terrain measurement error, inertial navigation positioning error, terrain matching algorithm, and terrain complexity. Among them, the acoustic terrain measurement error and inertial navigation positioning error are highly correlated with the performance of the underwater vehicle itself, while the terrain matching algorithm is highly correlated with the complexity of the terrain.
[0003] Terrain complexity is a relatively abstract concept, which comprehensively describes and expresses the complexity of the surface morphology. The quantitative indicators of terrain complexity in the existing technology are usually based on point descriptions, and a single indicator is mostly used for quantification. In addition, there is a complex coupling relationship between terrain complexity and terrain adaptability, which is difficult to describe with simple mathematical equations. This limits the AUV's terrain matching positioning based on terrain complexity, resulting in low terrain matching navigation accuracy. Summary of the invention
[0004] In view of the defects existing in the prior art, the present application provides a terrain matching adaptability evaluation method based on terrain complexity to solve the technical problems such as the low terrain matching navigation accuracy caused by the difficulty in quantifying the relationship between terrain complexity and terrain matching adaptability in the prior art.
[0005] In order to achieve the purpose of the above invention, the technical solution provided by the present invention is as follows:
[0006] A terrain matching adaptability evaluation method based on terrain complexity comprises the following steps:
[0007] S1, obtaining terrain data of the target area, and performing projection coordinate conversion on the terrain data to obtain a geographic coordinate system corresponding to the terrain data;
[0008] S2, calculate the terrain characteristic parameters of the target area, the terrain characteristic parameters include the local height difference P d , local standard deviation P s , local total curvature P c and local wrinkle degree P r ;
[0009] S3, calculate the terrain complexity P of the target area according to the terrain characteristic parameters of the target area tc ;
[0010] S4, construct a terrain matching simulation test, select a simulation area on the map, the simulation area includes multiple test areas, the test area includes multiple target areas, and define the regional terrain complexity as the terrain complexity P of all small areas in the area tc The average value of the regional terrain complexity of each test area is calculated, and simulation is performed in the simulation area to obtain the relationship between the regional terrain complexity and the terrain matching adaptability: when the regional terrain complexity is less than 0.2, the terrain matching adaptability is low; when the regional terrain complexity is between 0.2 and 0.6, the terrain matching adaptability is medium; when the regional terrain complexity is greater than 0.6, the terrain matching adaptability is high;
[0011] S5, select any underwater range and calculate the regional terrain complexity of the selected range, and evaluate the terrain matching adaptability of the selected range based on the relationship value between the terrain matching adaptability and the regional terrain complexity in S4. Step S1, in the implementation scheme, obtain the surveyed seabed elevation grid data, and perform projection coordinate conversion according to the regional position and the original data reference coordinate system;
[0012] In one embodiment, the terrain data is global seafloor elevation grid data.
[0013] In one embodiment, S2 specifically includes: dividing the target area into multiple square grids, z is the elevation value of the center point of the grid, the coordinate relationship between z and the center point of the grid is z=f(x, y), and the local height difference P d =max(z)-min(z), local standard deviation Ps=STDEV(z), local total curvature P c =-100×(d 2 z / dx 2 +d 2 z / dy 2 ), local wrinkle degree P r =A s / A p , where A s A is the surface area of the area formed by all grid center points within the window range, p The horizontal projection area corresponding to the area formed by all grid center points within the window range.
[0014] In one embodiment, S3 specifically includes:
[0015] S31, normalization processing terrain feature parameters:
[0016] Normalize the terrain characteristic parameters according to formula 1:
[0017]
[0018] In the formula, p k 、max(p k ) and min(p k ) represent the calculated value of a certain terrain characteristic parameter, the maximum value of a certain terrain characteristic parameter and the minimum value of a certain terrain characteristic parameter, respectively. k is a certain terrain characteristic parameter value after normalization, k is one of d, s, c or r;
[0019] S32. Calculate terrain complexity P tc :
[0020] N obtained through s1 d 、N s 、N c and N r Calculate the terrain complexity P according to formula 2 tc
[0021]
[0022] In one embodiment, S4 specifically includes:
[0023] S41, determine the terrain matching algorithm:
[0024] Using the TERCOM algorithm as the terrain matching algorithm, multiple sets of inertial navigation track sequences within the stage time are found in the terrain reference map, and the multiple sets of inertial navigation track sequences found are subjected to correlation analysis, and the sequence with the highest correlation is selected as the matching sequence at the current moment. The correlation analysis method uses the mean square error algorithm, and the optimal function is Formula 3:
[0025]
[0026] Among them, N represents the total number of points to be matched within the stage time, P j represents the position of the jth grid point in the search area, Δp i It represents the position deviation between the i-th point and the last point on the AUV navigation trajectory during the stage time, H i represents the i-th measured value measured during the stage time, h(p j +Δp i ) indicates the depth value read from the topographic reference map; the corresponding P when JMSD is minimum j is the optimal matching position;
[0027] S42. Select simulation area:
[0028] Select a simulation area on the map. The simulation area includes multiple test areas. The test area includes multiple target areas. Calculate the regional terrain complexity of each test area. Select multiple test areas with an average regional terrain complexity of 0.1 to 0.9 in the simulation area:
[0029] The regional terrain complexity refers to the terrain complexity of all target areas in the test area P tc The average value of the regional terrain complexity is calculated as Formula 4:
[0030]
[0031] Where N represents the number of target areas in the test area, p tcn is the terrain complexity of the nth target area, is the regional terrain complexity;
[0032] S43. Select a route that matches the terrain:
[0033] A route was constructed in the test area with different terrain complexity, and the initial and terminal positions of the route were randomly selected;
[0034] S44. Input AUV related parameters:
[0035] The parameters related to AUV and terrain matching include initial parameters and device error range. The initial parameters include initial position, and the error range includes terrain measurement error and inertial navigation device position error.
[0036] S45. Conduct simulation test:
[0037] Based on the route selected by S43 and the parameters input by S44, the TERCOM algorithm is used to perform terrain matching in areas with different terrain complexity. The terrain matching error is expressed in the form of standard deviation, and the relationship between regional terrain complexity and terrain matching adaptability is obtained.
[0038] In one embodiment, S5 specifically includes:
[0039] S51. Calculate the terrain complexity of any area:
[0040] Divide the selected range into multiple target areas, and calculate the terrain complexity P of each target area according to steps S1-S3. tc , the regional terrain complexity of the selected range is calculated by formula 4;
[0041] S52, determining the suitability of the selected area:
[0042] The terrain matching suitability of the selected range is evaluated based on the relationship between the terrain matching suitability and the regional terrain complexity in S4.
[0043] In one embodiment, the underwater is any sea area in the world.
[0044] Compared with the prior art, this application has at least the following beneficial effects:
[0045] The underwater terrain matching adaptability evaluation method based on terrain complexity of the present invention sequentially obtains terrain data and a geographic coordinate system of a target area, calculates terrain characteristic parameters of the target area, calculates the terrain complexity of the target area, constructs a terrain matching simulation test to obtain the relationship between regional terrain complexity and terrain matching adaptability, selects an arbitrary underwater range and calculates the regional average complexity of the selected range, and evaluates the terrain matching adaptability of the selected range, thereby being able to quantitatively evaluate the adaptability of underwater, especially seabed terrain, in combination with terrain complexity, thereby expanding the AUV's terrain matching positioning based on terrain complexity and improving the terrain matching navigation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 This is a flow chart of the terrain matching adaptability evaluation method in the embodiment of the present application.
[0047] Figure 2 is a schematic diagram of terrain feature parameters in an embodiment of the present application;
[0048] Figure 3 is a schematic diagram of terrain complexity in an embodiment of the present application;
[0049] Figure 4 It is a schematic diagram of routes within regions with different terrain complexities in an embodiment of the present application. DETAILED DESCRIPTION
[0050] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is described below by the specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.
[0051] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.
[0052] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms and should not be understood as indicating or implying relative importance. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".
[0053] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.
[0054] In order to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings.
[0055] like Figures 1 to 4 As shown, this embodiment takes the global seabed terrain as an example to provide an underwater terrain matching adaptability evaluation method based on terrain complexity, including the following steps:
[0056] S1. Obtain terrain data of the target area, the data type is terrain elevation grid data, and perform projection coordinate conversion on the terrain data to obtain the geographic coordinate system corresponding to the data.
[0057] In this embodiment, in order to verify the universality of the algorithm, global seabed topography data is selected. The data source is the ETOPO global grid terrain model data released by the US National Geophysical Data Center (NGDC). Based on this data, the adaptability of underwater vehicles for terrain matching in the global ocean can be evaluated.
[0058] S2. Calculate the terrain characteristic parameters of the target area, the terrain characteristic parameters including local height difference Pd, local standard deviation Ps, local total curvature Pc, and local wrinkle degree Pr.
[0059] The target area is divided into multiple square grids, z is the elevation value of the grid center point, and the coordinate relationship between z and the grid center point is z = f (x, y). The local height difference P d =max(z)-min(z), local standard deviation P s =STDEV(z), local total curvature P c =-100×(d 2 z / dx2 +d 2 z / dy 2 ), local wrinkle degree P r =A s / A p , where As is the surface area of the area formed by all grid center points within the window range, A p The horizontal projection area corresponding to the area formed by all grid center points within the window range.
[0060] In this embodiment, the calculation window is divided into nine square grids in a nine-square format. The terrain characteristic parameters calculated based on the ETOPO global terrain data model are as follows: Figure 1 shown.
[0061] S3. Calculate the terrain complexity P of the target area according to the terrain characteristic parameters of the target area tc .
[0062] S3 specifically includes:
[0063] S31. Normalization of terrain feature parameters:
[0064] In view of the inconsistency of the value ranges of the calculation results of different terrain characteristic parameters, in order to effectively balance the influence of different terrain characteristic parameters on the calculation results of terrain complexity, the terrain characteristic parameters are first normalized according to Formula 1, and the calculated values of each terrain characteristic parameter are all normalized to the numerical range of [0, 1].
[0065]
[0066] In the formula, p k 、max(p k ) and min(p k ) represent the calculated value of a certain terrain characteristic parameter, the maximum value of a certain terrain characteristic parameter and the minimum value of a certain terrain characteristic parameter, respectively. k is a certain terrain characteristic parameter value after normalization, and k is one of d, s, c or r.
[0067] S32. Calculate terrain complexity P tc :
[0068] Terrain complexity P tc The terrain complexity P is calculated by averaging four normalized terrain characteristic parameter data. tc As shown in formula 2:
[0069]
[0070] The terrain complexity calculated in this embodiment is as follows: Figure 3 shown.
[0071] S4. Construct terrain matching simulation, construct terrain matching simulation test, select simulation area on the map, the simulation area includes multiple test areas, the test area includes multiple target areas, define regional terrain complexity as the terrain complexity P of all small areas in the area tc The average value of the terrain matching is taken, and simulation is carried out in the simulation area to obtain the relationship between the terrain matching adaptability and the regional terrain complexity.
[0072] S4 specifically includes:
[0073] S41. Determine a terrain matching algorithm and an optimal matching position.
[0074] In order to quantitatively evaluate the adaptability of terrain matching, it is necessary to establish a mathematical relationship between the terrain matching error and the terrain complexity. Therefore, this paper quantifies it by constructing a terrain matching simulation experiment. In the simulation experiment, it is necessary to first determine the terrain matching algorithm.
[0075] This embodiment selects the TERCOM (Terrain Contour Matching) algorithm as the terrain matching algorithm, finds multiple sets of inertial navigation trajectory sequences within the stage time in the terrain reference map, performs correlation analysis on the multiple sets of inertial navigation trajectory sequences found, and selects the sequence with the highest correlation as the matching sequence at the current moment. In the algorithm, the correlation analysis method usually adopts the mean square difference (MSD) algorithm, and the optimal function is Formula 3:
[0076]
[0077] Among them, N represents the total number of points to be matched within the stage time, P j represents the position of the jth grid point in the search area, Δp i It represents the position deviation between the i-th point and the last point on the AUV navigation trajectory during the stage time, H i represents the i-th measured value measured during the stage time, h(p j +Δp i ) indicates that the depth value is read from the terrain reference map. The smaller the JMSD value, the higher the correlation. Therefore, the optimal matching position is the P corresponding to the minimum JMSD. j , that is, the best matching position is the real position.
[0078] S42, selecting a simulation area on the map, the simulation area includes multiple test areas, the test area includes multiple target areas, calculating the regional terrain complexity of each test area, and selecting multiple test areas with an average regional terrain complexity of 0.1 to 0.9 in the simulation area:
[0079] The regional terrain complexity refers to the terrain complexity of all target areas in the test area P tc The average value of the regional terrain complexity is calculated as Formula 4:
[0080]
[0081] Where N represents the number of target areas in the test area, p tcn is the terrain complexity of the nth target area, is the complexity of regional terrain.
[0082] S43. Select a route that matches the terrain:
[0083] A route is constructed in an area with different terrain complexity. The initial position and the end position of the route are randomly selected. In order to reduce the calculation data, all route paths in this embodiment are set as straight lines. The relevant schematic diagram is as follows Figure 4 In other embodiments, the flight path may be any linear path.
[0084] S44. Input the initial parameters and error range of AUV:
[0085] Since the terrain matching error is affected by the measurement error of the AUV navigation device, it is necessary to define the initial parameters in combination with the actual situation. In this embodiment, it is assumed that the AUV initial position error is 0, the terrain measurement error is 0.5%, and the inertial navigation device position error is 5 meters. In other embodiments, the initial parameters and error ranges can also be other values, and the results are the same as in this embodiment.
[0086] S45. Conduct simulation test:
[0087] Based on the route selected in S43 and the parameters input in S44, the TERCOM algorithm is used to perform terrain matching in areas with different terrain complexities, and the terrain matching error is expressed in the form of standard deviation. In this embodiment, the relationship between the regional terrain complexity P and the terrain matching adaptability obtained through simulation experiments is shown in Table 1. It can be seen from the table that when the regional terrain complexity is less than 0.2, the terrain matching adaptability is low; when the regional terrain complexity is between 0.2 and 0.6, the terrain matching adaptability is medium; when the regional complexity is greater than 0.6, the terrain matching adaptability is high.
[0088] Table 1 Relationship between regional terrain complexity and terrain matching error
[0089]
[0090] S5. Select an arbitrary underwater range and calculate the regional terrain complexity of the selected range, and evaluate the terrain matching adaptability of the selected range based on the relationship value between the terrain matching adaptability and the regional terrain complexity in S4.
[0091] S5 specifically includes:
[0092] S51. Calculate the terrain complexity of the area:
[0093] In this embodiment, any range is selected in the global ocean area, the selected range is divided into multiple target areas, and the terrain complexity P of each target area is calculated according to steps S1-S3. tc , the terrain complexity of the selected area is calculated by formula 4. Figure 2 Taking the red frame area as an example, the average terrain complexity P of the red frame area is calculated to be 0.76;
[0094] S52, evaluating the terrain matching adaptability of the selected range based on the relationship between the terrain matching adaptability and the regional terrain complexity P in S4. In this embodiment, the terrain matching adaptability of the red frame area is a strong adaptation area.
[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.
Claims
1. An underwater terrain matching adaptability evaluation method based on terrain complexity, characterized in that: The following steps are involved: S1, obtaining terrain data of the target area, and performing projection coordinate conversion on the terrain data to obtain a geographic coordinate system corresponding to the terrain data; S2, calculate the terrain characteristic parameters of the target area, the terrain characteristic parameters include the local height difference P d , local standard deviation P s , local total curvature P c and local wrinkle degree P r ; S3, calculate the terrain complexity P of the target area according to the terrain characteristic parameters of the target area tc ; S4, construct a terrain matching simulation test, select a simulation area on the map, the simulation area includes multiple test areas, the test area includes multiple target areas, and define the regional terrain complexity as the terrain complexity P of all small areas in the area tc The average value of the regional terrain complexity of each test area is calculated, and simulation is performed in the simulation area to obtain the relationship between the regional terrain complexity and the terrain matching adaptability: when the regional terrain complexity is less than 0.2, the terrain matching adaptability is low; when the regional terrain complexity is between 0.2 and 0.6, the terrain matching adaptability is medium; when the regional terrain complexity is greater than 0.6, the terrain matching adaptability is high; S5, selecting an arbitrary underwater range and calculating the regional terrain complexity of the selected range, and evaluating the terrain matching adaptability of the selected range based on the relationship value between the terrain matching adaptability and the regional terrain complexity in S4.
2. The underwater terrain matching method based on terrain complexity according to claim 1, characterized in that: The terrain data is the global seabed elevation grid data.
3. The underwater terrain matching method based on terrain complexity according to claim 1, characterized in that: S2 specifically includes: dividing the target area into multiple square grids, z is the elevation value of the center point of the grid, the coordinate relationship between z and the center point of the grid is z=f(x, y), and the local height difference P d =max(z)-min(z), local standard deviation P s =STDEV(z), local total curvature P c =-100×(d 2 z / dx 2 +d 2 z / dy 2 ), local wrinkle degree P r =A s / A p , where As is the surface area of the area formed by all grid center points within the window range, A p The horizontal projection area corresponding to the area formed by all grid center points within the window range.
4. The underwater terrain matching method based on terrain complexity according to claim 3 is characterized in that: S3 specifically includes: S31, normalization processing terrain feature parameters: Normalize the terrain characteristic parameters according to formula 1: In the formula, p k 、max(p k ) and min(p k ) represent the calculated value of a certain terrain characteristic parameter, the maximum value of a certain terrain characteristic parameter and the minimum value of a certain terrain characteristic parameter, respectively. k is a certain terrain characteristic parameter value after normalization, k is one of d, s, c or r; S32. Calculate terrain complexity P tc : N obtained through s1 d 、N s 、N c and N r Calculate the terrain complexity P according to formula 2 tc 5. The terrain matching method based on terrain complexity according to claim 3, characterized in that: S4 specifically includes: S41, determine the terrain matching algorithm: Using the TERCOM algorithm as the terrain matching algorithm, multiple sets of inertial navigation track sequences within the stage time are found in the terrain reference map, and the multiple sets of inertial navigation track sequences found are subjected to correlation analysis, and the sequence with the highest correlation is selected as the matching sequence at the current moment. The correlation analysis method uses the mean square error algorithm, and the optimal function is Formula 3: Among them, N represents the total number of points to be matched within the stage time, P j represents the position of the jth grid point in the search area, Δp i It represents the position deviation between the i-th point and the last point on the AUV navigation trajectory during the stage time, H i represents the i-th measured value measured during the stage time, h(p j +Δp i ) indicates the depth value read from the topographic reference map; the corresponding P when JMSD is minimum j is the optimal matching position; S42. Select simulation area: Select a simulation area on the map. The simulation area includes multiple test areas. The test area includes multiple target areas. Calculate the regional terrain complexity of each test area. Select multiple test areas with an average regional terrain complexity of 0.1 to 0.9 in the simulation area: The regional terrain complexity refers to the terrain complexity of all target areas in the test area P tc The average value of the regional terrain complexity is calculated as Formula 4: Where N represents the number of target areas in the test area, p tcn is the terrain complexity of the nth target area, is the regional terrain complexity; S43. Select a route that matches the terrain: A route was constructed in the test area with different terrain complexity, and the initial and terminal positions of the route were randomly selected; S44. Input AUV related parameters: The parameters related to AUV and terrain matching include initial parameters and device error range. The initial parameters include initial position, and the error range includes terrain measurement error and inertial navigation device position error. S45. Conduct simulation test: Based on the route selected by S43 and the parameters input by S44, the TERCOM algorithm is used to perform terrain matching in areas with different terrain complexity. The terrain matching error is expressed in the form of standard deviation, and the relationship between regional terrain complexity and terrain matching adaptability is obtained.
6. The terrain matching method based on terrain complexity according to claim 5, characterized in that: S5 specifically includes: S51. Calculate the terrain complexity of any area: Divide the selected range into multiple target areas, and calculate the terrain complexity P of each target area according to steps S1-S3. tc , the regional terrain complexity of the selected range is calculated by formula 4; S52, determining the suitability of the selected area: The terrain matching suitability of the selected range is evaluated based on the relationship between the terrain matching suitability and the regional terrain complexity in S4.
7. The terrain matching method evaluation based on terrain complexity according to claim 1, characterized in that: The underwater refers to any sea area in the world.