Terrain navigability evaluation method coupled with underwater aided navigation algorithm

Abstract

The invention relates to the technical field of marine surveying and mapping, and aims to solve the problem of inaccurate evaluation caused by separation of an existing terrain navigability evaluation method and a matching algorithm. The invention provides a terrain navigability evaluation method coupled with an underwater aided navigation algorithm, which is characterized by comprising the following steps of: constructing a circular ring window by taking a point to be evaluated as a center; discretized sampling is carried out on the window to obtain a water depth value sequence; calculating a mean square error sequence of the center point and the peripheral point sequence; and taking the standard deviation of the mean square deviation sequence as a navigability evaluation index. According to the method, the similarity measurement process of the matching algorithm is simulated, so that the evaluation result is strongly correlated with the positioning precision, and the method can be used for intelligent path planning and real-time navigation area optimization of the underwater vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of marine surveying and mapping, and relates to a terrain navigability evaluation method coupled with an underwater auxiliary navigation algorithm. BACKGROUND

[0002] Underwater terrain auxiliary navigation is an important technical means for realizing long-time autonomous navigation with high precision when a submarine cannot receive satellite signals. The positioning performance is closely related to the terrain features of the navigation area, and therefore, the navigability of underwater terrain needs to be scientifically and accurately evaluated to select the most suitable area for matching navigation and thus guarantee the reliability of the entire navigation system.

[0003] Currently, the mainstream underwater terrain navigability evaluation method generally relies on inherent statistical features of terrain, such as terrain standard deviation, roughness, correlation coefficient and information entropy, and builds an evaluation index system (as described in the paper “Terrain navigability analysis in underwater terrain matching navigation”) based on the same. However, these methods have a common and fundamental limitation: they do not consider the similarity measurement criteria actually used by specific matching algorithms in the matching process when evaluating terrain. In other words, the evaluation system is “disconnected” from the matching algorithm. This disconnection leads to an inability to accurately quantify the degree to which terrain distinguishes between specific matching algorithms, making the correlation between the evaluation results and the final positioning accuracy not strong, and thus seriously affecting the accuracy and effectiveness of matching area selection. SUMMARY

[0004] In order to overcome the shortcomings of existing terrain navigability evaluation methods, the application provides a terrain navigability evaluation method coupled with an underwater auxiliary navigation algorithm.

[0005] The technical solution of the application is as follows:

[0006] A terrain navigability evaluation method coupled with an underwater auxiliary navigation algorithm, comprising the following steps:

[0007] Step a: constructing a circular window for searching the terrain to be evaluated with the geographical position point to be evaluated as the center;

[0008] Step b: after determining the terrain search window to be evaluated, discretely sampling the circular window and determining the water depth value corresponding to each sampling point according to the digital terrain reference map to form a water depth value sequence to be evaluated;

[0009] Step c: selecting a plurality of geographical position points within a predetermined range around the geographical position point in step a, and repeating step b for each point to obtain the corresponding water depth value sequence to be evaluated;

[0010] Step d, calculate the mean square error of the difference between the sequence of the center point and the sequence of each peripheral point, and form a sequence of mean square errors;

[0011] Step e, calculate the standard deviation of the sequence of mean square errors, and take the standard deviation as the terrain navigability evaluation index of the geographic position point.

[0012] Step f, after calculating the terrain navigability evaluation index value of each point in the evaluation area, the Tukey test with variable parameters is used to remove outliers, and finally the navigability evaluation index distribution of the matching area is obtained.

[0013] In step a, the method for constructing the annular window is:

[0014] First, determine the resolution of the digital terrain reference map , and take it as the search step; secondly, based on the classic underwater aided navigation algorithm (TERCOM algorithm), take the geographic position point as the center, take 3 times the inertial navigation system error limit as the radius, take the search step as the interval, sample in the digital terrain reference map to obtain the sequence of water depth values ; thirdly, find the minimum value of , and subtract the diving depth of the submarine as the potential closest distance of the submarine from the seabed:

[0015]

[0016] In the formula, , it represents the closest distance of the submarine from the seabed within the range of the geographic position point A as the center and 3 times the inertial navigation system error limit as the radius.

[0017] Finally, according to the ratio k of the scanning width to the depth of the multi-beam in the underwater aided navigation system, the radius of the annular window is determined .

[0018]

[0019] In step b, the method for discretely sampling the annular window to obtain the sequence of water depth values to be evaluated is:

[0020] First, divide the annular window into N equal parts with the resolution of the digital terrain reference map as the step:

[0021]

[0022] Then, calculate the water depth values of the N points on the annular window corresponding to the digital terrain reference map respectively :

[0023] ​

[0024] In the formula, represents the first part of the circular window which is discretized, the function represents the digital terrain reference map value corresponding to the position

[0025] In the step f, the specific method of removing outliers by using the Tukey test with variable parameters is as follows:

[0026] First, the maximum value , the minimum value and the average value of the terrain navigability evaluation index in the evaluation range are calculated;

[0027]

[0028] In the formula, represents the upper limit of the outlier of the navigability evaluation index, represents the lower limit of the outlier of the navigability evaluation index; respectively represent the lower quartile and the upper quartile of the sequence of the navigability evaluation index.

[0029] The present application provides a terrain navigability evaluation method which is deeply coupled with matching algorithm, aiming to solve the problem of "evaluation disconnection from algorithm" caused by the fact that the existing evaluation method does not consider the specific requirements of the matching algorithm. The method standardizes the extraction of terrain features by constructing a circular window decoupled from the track; quantifies the adaptability of the terrain to a specific algorithm by constructing a mean square difference operator based on the similarity measurement criterion of the matching algorithm; dynamically removes outliers in the evaluation index by introducing a variable parameter Tukey test to achieve accurate evaluation. The present application can effectively solve the problem that the existing evaluation index has weak correlation with the final positioning accuracy. The method of the present application is logically rigorous and accurate in evaluation, providing a reliable basis for intelligent path planning and real-time navigation area optimization of a submerged vehicle, and has high practical application value. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a basic flowchart of the present application.

[0031] Figure 2 is a schematic diagram for determining a circular window according to a digital terrain reference map, inertial navigation error and other information.

[0032] Figure 3 is a schematic diagram for obtaining a terrain water depth sequence to be evaluated according to the circular window. Detailed Implementation

[0033] The following description, in conjunction with the embodiments and accompanying drawings, further explains the specific implementation of the present invention, but is not intended to limit the scope of this embodiment.

[0034] The basic flow of the terrain navigability evaluation method of the present invention coupled with an underwater assisted navigation algorithm is as follows: Figure 1 As shown.

[0035] The digital terrain reference map used in this embodiment is a 900m × 900m resolution terrain matching reference map provided by the U.S. Ocean Depth Mapping Committee. This embodiment provides a terrain navigability evaluation method for a deep-coupled underwater terrain-assisted navigation algorithm. It can take into account the performance of the multibeam bathymetry system of the assisted navigation algorithm, the error limit of the inertial navigation system, the diving depth of the submersible, the resolution of the digital terrain reference map used, and the inherent characteristics of the terrain matching area, thereby obtaining a navigability evaluation result that is strongly correlated with the accuracy of the matching navigation. The specific steps are as follows:

[0036] Step a: Collect and prepare navigation data and terrain matching reference map data for the submersible. This data includes the swath aspect ratio of the multibeam echo sounder carried by the submersible, the error limit of the inertial navigation system, the submersible's daily diving depth, and digital terrain reference maps, etc.

[0037] First, select geographical location point A to be evaluated in the digital terrain baseline map, such as... Figure 2 Secondly, based on the preset error limit σ of the inertial navigation system, a search range with center A and a radius of 3σ is determined; thirdly, the resolution of the digital terrain reference map is used as the basis for the search range. Using a step size, iterate through the range to find the water depth values. After finding the shallowest point A′, according to the formula The distance between the submersible and the seabed when it is located at point A′ is obtained by solving the equation; finally, based on the swath width-to-depth ratio k of the multibeam echo sounder, the distance is calculated according to the formula... Solving for the radius of the annular window .

[0038] Step b, firstly, at the resolution of the digital terrain reference map. Let the step size be used, and then use the formula. Divide the annular window into N equal parts; then, center on A. A circular window with radius 1 is used to sample water depth sequences from a digital topographic reference map, as shown in Figure 1. Figure 2 .

[0039] Step c, with A as the center, Using a step size of 3σ and a search range of 3σ, the circular window is moved to obtain water depth sequence 2, water depth sequence 3, ..., water depth sequence n, respectively. Figure 3.

[0040] Step d, respectively calculate the mean squared difference of the water depth value sequence 1 corresponding to the circular window of point A and the water depth value sequence 2, water depth sequence 3, …, water depth sequence n of each peripheral point, form the mean squared difference sequence M.

[0041] Step e, calculate the standard deviation of the mean squared difference sequence M, get , and the standard deviation as the terrain navigability evaluation index of geographical position point A.

[0042] Step f, first, in the range of digital terrain reference map, move A point with as the step size, and repeat steps a-e to calculate the terrain navigability evaluation index value of each point; then, use the formula to remove the outliers; finally, take the navigability evaluation index after removing the outliers as the navigability evaluation index distribution in the range of the digital terrain reference map.

[0043] To verify the effectiveness of the present application, 50 terrain profiles are randomly selected in the digital terrain reference map, the matching navigation process of the underwater vehicle in the region is simulated, and the correlation of the final positioning error under the algorithm of the present application and various traditional evaluation indexes is counted, and the experimental results are shown in Table 1.

[0044] Through the above steps, compared with the best traditional index (roughness, 0.424), the correlation of the navigability evaluation index and the final positioning error is improved by more than 22%; compared with the average level of all traditional indexes (0.292), the improvement is as high as 77%. This shows that the present application can fully consider the characteristics of the underwater aided navigation algorithm and the digital terrain reference map, and comprehensively evaluate the navigability of the terrain.

[0045] Table 1. Comparison of terrain navigability characteristic values

[0046]

[0047] In the table, MSD (Mean Squared Difference) refers to the mean squared difference, which is used to measure the dispersion degree of the evaluation index value.

[0048] It should be noted that the present application is described by examples, and various obvious modifications, replacements and equivalent transformations of the technical solutions described in the present application can be made without departing from the spirit and scope of the present application.

[0049] For example, those skilled in the art can understand that, although the present application is illustrated by taking the TERCOM algorithm as an example, the core evaluation idea is also applicable to other matching algorithms based on the similarity measurement of terrain profiles, such as the iterative closest point algorithm. In addition, the specific shape of the circular ring window, the determination method of the sampling step, or the specific algorithm of the outlier elimination can be adaptively adjusted without deviating from the core idea of the present application.

Claims

1. A method for evaluating terrain navigability coupled with an underwater assisted navigation algorithm, characterized in that, Includes the following steps: Step a: Construct a circular window centered on the geographic location to be evaluated for searching the terrain to be evaluated; Step b: After determining the terrain search window to be evaluated, the circular window is discretized and sampled, and the water depth value corresponding to each sampling point is determined according to the digital terrain reference map to form a sequence of water depth values ​​to be evaluated. Step c: Using the geographical location point described in step a as the center, select multiple geographical locations within a preset range around it, and repeat step b for each point to obtain its corresponding sequence of water depth values ​​to be evaluated. Step d: Calculate the root mean square error of the difference between the water depth value sequence to be evaluated at the center point and the water depth value sequences to be evaluated at each surrounding point, and form a root mean square error sequence. Step e: Calculate the standard deviation of the mean squared error sequence and use the standard deviation as an evaluation index of the terrain navigability of the geographical location points; Step f: After calculating the terrain navigability evaluation index values ​​for each point in the evaluation area, a Tukey test with varying parameters is used to remove outliers, and finally the distribution of navigability evaluation indexes for the matching area is obtained.

2. The method according to claim 1, characterized in that, In step a, the method for constructing the annular window is as follows: First, determine the resolution of the digital terrain baseline map. This is used as the search step size; secondly, based on the TERCOM algorithm, with the geographical location as the center and a radius of 3 times the inertial navigation system error limit, the search step size is... At intervals, samples are taken from the digital topographic baseline map to obtain a sequence of water depth values. ; Again, seek The minimum value, minus the diving depth of the submersible. As the potential closest distance between a submersible and the seabed. Finally, based on the ratio k of the multibeam sweep width to depth in the underwater assisted navigation system, the radius of the annular window is determined. .

3. The method according to claim 2, characterized in that, Determine the radius of the annular window The method is as follows: .

4. The method according to claim 1 or 2, characterized in that, In step b, the method for discretizing the circular window to obtain the water depth value sequence to be evaluated is as follows: First, using the resolution of the digital terrain reference map... Divide the annular window into N equal parts using a step size; then, calculate the water depth values ​​on the digital topographic reference map corresponding to the locations of the N points on the annular window. .

5. The method according to claim 4, characterized in that, The calculation method is as follows: In the formula, The discretization of the annular window represents the first... Part, function Indicates position The corresponding digital topographic reference map values.

6. The method according to claim 1 or 2, characterized in that, In step f, the specific method for removing outliers using the Tukey test with varying parameters is as follows: First, calculate the maximum value of the terrain navigation evaluation index within the assessment area. Minimum value and average Then, based on the Tukey test, outliers of the navigation evaluation indicators are determined according to the maximum, minimum and average values ​​of the navigation evaluation indicators; finally, outliers in the terrain navigation evaluation indicators are removed.

7. The method according to claim 1 or 2, characterized in that, The methods for identifying outliers are as follows: In the formula, This represents the upper limit of outliers for the navigationability evaluation index. This indicates the lower limit of outliers for the navigability evaluation index; These represent the lower quartile and upper quartile of the navigable evaluation index sequence, respectively.

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