CN108896040B - Inertia/gravity combined navigation method and system for sky-sea integrated underwater vehicle - Google Patents
Inertia/gravity combined navigation method and system for sky-sea integrated underwater vehicle Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
Abstract
The invention discloses a sky-sea integrated underwater vehicle inertia/gravity combined navigation method and a system, wherein the method is used for improving the matching efficiency of the underwater vehicle inertia/gravity combined navigation based on a hierarchical neighborhood threshold search principle; the method comprises the steps of performing rough search matching with a larger grid interval number, setting a threshold value, performing matching comparison on a plurality of neighborhood points around the selected optimal matching point of each rough search line to obtain a temporary optimal matching point in the range near each rough search line, and finally comparing all the obtained temporary optimal matching points to obtain the optimal matching point in the whole search range. The method has the advantages of high matching efficiency, high positioning precision, low requirement on computer performance and the like.
Description
Technical Field
The invention belongs to the technical field of spaceflight and oceans, and particularly relates to a sky and sea integrated underwater vehicle inertia/gravity combined navigation method and system.
Background
Sky-sea integrated marine navigation (fusion of multi-source information such as space base, sea base and the like) is a main trend of development of underwater vehicle navigation systems. At present, an underwater vehicle Navigation System is mainly an Inertial Navigation System (INS) which can provide favorable conditions for underwater vehicle Navigation and weapon System accurate striking, but the INS System has the defect that errors are accumulated along with time, so that external correction is needed. So far, a passive aided navigation system formed by combining a geophysical field and an INS system is always an international research hotspot for effectively inhibiting the problem of error accumulation of the INS system. The current passive auxiliary navigation technology mainly comprises: geomagnetic matching, terrain matching, gravity matching, and the like. The geomagnetic field changes for a long time and a short time, so that the accuracy of the geomagnetic map cannot meet higher requirements, and the magnetic measurement technology has the limitations of magnetic interference and the like. Terrain research was developed earlier, particularly land-based terrain-matched navigation techniques have been applied to aircraft navigation, but underwater terrain-matched navigation has been developed relatively late. Because sound waves need to be transmitted outwards and the deep sea landforms cannot be accurately detected under the complex sea conditions of the sonar survey landforms, the existing underwater landform matching navigation technology is only suitable for shallow sea areas. However, the marine gravity matching navigation realizes navigation and positioning according to gravity difference of different positions of the earth, an underwater vehicle is not required to float out of or approach the water surface, energy is not radiated outwards during measurement, and the earth gravity field is kept stable for a long time, so that the accurate, autonomous and continuous long-endurance positioning of the underwater vehicle is expected to be realized.
The core problem of the realization of the gravity matching technology is to solve key technologies such as a high-precision and high-resolution gravity reference diagram, a high-precision gravity measurement system, a gravity matching positioning algorithm and the like. Since the last century, numerous scholars have proposed various gravity matching algorithms, of which the Tercom algorithm is more prevalent. The method has the advantages of simple and reliable calculation, higher positioning precision and the like; the method has the disadvantages that a global traversal search strategy is adopted, the calculation amount is large, the calculation efficiency is low, the requirement on the carrier track is high, and the error is increased sharply when large deviation exists in the course. Therefore, how to improve the positioning accuracy and the matching efficiency of the Tercom algorithm is a research hotspot in the field of current underwater navigation. Zhao Jian Hu introduces a matching criterion based on Hausdorff distance into a Tercom algorithm, and proposes ideas and algorithms for determining an optimal rotation angle and realizing precise matching of an adaptive sequence by increasing rotation change and self-adaption, so that the matching navigation precision and reliability are effectively improved; yan Li et al, based on the Tercom algorithm, develop a gravity matching simulation study and prove that terrain roughness and slope variance have strong correlation with the positioning accuracy of the Tercom algorithm. In summary, most of the scholars mainly develop research on improving underwater navigation accuracy, and relatively few research on improving underwater navigation matching efficiency is performed.
Disclosure of Invention
The technical problem of the invention is solved: the defects of the prior art are overcome, the inertia/gravity combined navigation method and the system for the sky-sea integrated underwater vehicle are provided, the defect that the traditional Tercom algorithm is low in point-by-point traversal searching efficiency is overcome, and the matching efficiency of the inertia/gravity combined navigation for the sky-sea integrated underwater vehicle is improved.
In order to solve the technical problem, the invention discloses a sky-sea integrated underwater vehicle inertia/gravity combined navigation method, which comprises the following steps:
in the determined search range, performing coarse search matching by setting grid interval number to obtain a coarse search optimal matching point;
according to a set threshold value, matching and comparing a plurality of neighborhood points around the rough search optimal matching point to obtain rough search temporary optimal matching points corresponding to the rough search optimal matching points;
comparing the temporary optimal matching points of the rough search to obtain the optimal matching points in the whole search range;
and outputting the best matching point as a navigation result.
In the above method for integrated navigation of sky and sea underwater vehicle by inertia/gravity, the method further comprises:
determining position information output by an INS at the current moment and an INS estimation error corresponding to the position information;
and determining a search range in the gravity reference map according to the position information and the INS estimation error corresponding to the position information.
In the above method for integrated inertia/gravity navigation of a sky-sea integrated underwater vehicle, determining a search range in a gravity reference map according to the position information and an INS estimation error corresponding to the position information includes:
and determining a search range in the gravity reference map by taking the position indicated by the position information as a circle center and the multiple of the INS estimation error as a radius.
In the above method for integrated inertia/gravity navigation of a sky-sea integrated underwater vehicle, the gravity reference map includes: rows and columns; where the rows represent latitudes and the columns represent longitudes.
In the above method for integrated navigation of inertia/gravity of the sky-sea integrated underwater vehicle, the coarse search and matching are performed at a set grid interval number within a certain search range to obtain a coarse search optimal matching point, and the method includes:
selecting at least one rough search line within the determined search range according to a first set interval;
and according to a terrain contour matching method, performing coarse search on each coarse search line according to a second set interval to obtain the best matching point of each coarse search line.
In the inertia/gravity combined navigation method for the sky-sea integrated underwater vehicle, the step of comparing a plurality of neighborhood points around the rough search optimal matching point according to a set threshold to obtain rough search temporary optimal matching points corresponding to the rough search optimal matching points includes:
and according to the set threshold, carrying out matching comparison on a plurality of neighborhood points around the optimal matching point of each rough search line to obtain a temporary optimal matching point in the range near each rough search line as a rough search temporary optimal matching point.
Correspondingly, the invention also discloses a sky and sea integrated underwater vehicle inertia/gravity combined navigation system, which comprises:
the coarse searching module is used for performing coarse searching matching in a determined searching range by setting grid interval number to obtain a coarse searching optimal matching point;
the screening module is used for carrying out matching comparison on a plurality of neighborhood points around the rough search optimal matching point according to a set threshold value to obtain rough search temporary optimal matching points corresponding to the rough search optimal matching points;
the comparison module is used for comparing the temporary optimal matching points of each rough search to obtain the optimal matching points in the whole search range;
and the output module is used for outputting the optimal matching point as a navigation result.
The invention has the following advantages:
1) the underwater inertia/gravity combined navigation matching efficiency is high;
2) the underwater gravity matching positioning precision is high;
3) the underwater gravity matching navigation has high reliability;
4) the underwater gravity matching process has clear physical meaning.
Drawings
FIG. 1 is a flowchart illustrating steps of a combined inertial/gravity navigation method for a sky-sea integrated underwater vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic view of an embodiment of an underwater inertial/gravity integrated navigation process;
FIG. 3 is a schematic diagram illustrating comparison of algorithm positioning accuracy and efficiency under different grid spacing numbers and neighborhoods in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an algorithm statistical information analysis under different grids and neighborhoods in an embodiment of the present invention;
wherein fig. 3(a) shows the number of 3 mesh intervals, fig. 3(b) shows the number of 4 mesh intervals, fig. 3(c) shows the number of 5 mesh intervals, and fig. 3(d) shows the number of 6 mesh intervals; fig. 4(a) shows an average positioning accuracy comparison, fig. 4(b) shows a positioning accuracy standard deviation comparison, fig. 4(c) shows a matching registration ratio comparison, and fig. 4(d) shows a positioning time comparison.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart illustrating steps of a combined inertial/gravity navigation method for a sky-sea integrated underwater vehicle according to an embodiment of the present invention is shown. In this embodiment, the integrated navigation method of inertia/gravity of the sky-sea integrated underwater vehicle includes:
and 101, performing coarse search matching by setting grid intervals within the determined search range to obtain a coarse search optimal matching point.
And 102, according to a set threshold value, matching and comparing a plurality of neighborhood points around the rough search optimal matching point to obtain rough search temporary optimal matching points corresponding to the rough search optimal matching points.
And 103, comparing the temporary optimal matching points of the rough search to obtain the optimal matching points in the whole search range.
And 104, outputting the optimal matching point as a navigation result.
In a preferred embodiment of the present invention, before step 101, the method for integrated inertia/gravity navigation of a sky-sea integrated underwater vehicle may further include: determining position information output by an INS at the current moment and an INS estimation error corresponding to the position information; and determining a search range in the gravity reference map according to the position information and the INS estimation error corresponding to the position information. Wherein, the gravity benchmark picture includes: rows and columns; the rows represent latitudes and the columns represent longitudes.
Preferably, the determining a search range in the gravity reference map according to the position information and the INS estimation error corresponding to the position information may specifically include: and determining a search range in the gravity reference map by taking the position indicated by the position information as a center of a circle and a multiple of the INS estimation error (for example, 3 times of the INS estimation error) as a radius.
It should be noted that, when determining the search range, the radius (multiple of the INS estimation error) may be selected to be an appropriate value according to the actual situation, which is not limited in this embodiment.
In a preferred embodiment of the present invention, the step 101 may specifically include: selecting at least one rough search line within the determined search range according to a first set interval; and according to a terrain contour matching method, performing coarse search on each coarse search line according to a second set interval to obtain the best matching point of each coarse search line.
The first setting interval and the second setting interval may be selected to have appropriate values according to actual conditions, which is not limited in this embodiment.
In a preferred embodiment of the present invention, the step 102 may specifically include: and according to the set threshold, carrying out matching comparison on a plurality of neighborhood points around the optimal matching point of each rough search line to obtain a temporary optimal matching point in the range near each rough search line as a rough search temporary optimal matching point.
Based on the above embodiments, the following description will be made with reference to a specific example.
In this embodiment, the inertia/gravity combined navigation method for the sky-sea integrated underwater vehicle is based on a hierarchical neighborhood threshold search principle to improve the inertia/gravity combined navigation matching efficiency of the underwater vehicle.
In the embodiment, the research data is derived from a global sea-land database (http:// topex. ucsd. edu /) generated by a plurality of technologies such as united space flight, ocean and the like, the longitude and latitude range of the data is (longitude 112-116 degrees E, latitude 10-11 degrees N), and the original seabed gravity anomaly resolution is 1 'x 1'.
The underwater gravity matching precision is mainly determined by a matching algorithm and gravity field characteristic parameters. The existing gravity matching algorithm is a multi-source of a Terrain matching algorithm, wherein TERCOM (Tertain content matching), ICCP (iterative closed content Point), PDAF (Point availability Data association), and the like are batch processing related algorithms, and SITAN (Sand interferometric Tertain-air Navigation) and PF (particle Filter) algorithms are continuous matching algorithms based on Extended Kalman Filter (EKF) and direct probability criterion, respectively.
The gravity field characteristic parameters are key factors influencing underwater navigation precision and matching probability, and mainly comprise the following steps: gravity field standard deviation, gradient standard deviation, kurtosis coefficient, roughness, information entropy, correlation coefficient and the like. The invention provides a layered neighborhood threshold search method based on the characteristics of continuous distribution of gravity fields and slow change of spatial distribution, and aims to improve the matching efficiency of a Tercom algorithm. The calculation principle and the algorithm flow of the hierarchical neighborhood threshold search method are shown in fig. 2.
Determining the coordinates of the starting position A and the target position B, and planning the course.
And secondly, after the underwater vehicle enters the matching area, when the gravity field measurement sequence is long enough, a layered neighborhood threshold search method is adopted. Firstly, estimating the radius of a search range according to the INS error at the current moment, and performing coarse search matching by using a larger grid in the 3 sigma (INS estimation error corresponding to position information) error search range to obtain the optimal matching point of each coarse search line; secondly, selecting a plurality of neighborhood points around the optimal matching point of the rough search line for matching comparison to obtain a temporary optimal matching point in the range near each rough search line, and setting a threshold value in the matching process to improve the selection standard of the matching point; and finally, comparing and analyzing the obtained temporary optimal matching points, and determining the optimal matching points in the search range.
In the matching search process, it is assumed that the size of the search area is M × M grid, g (x, y) is the gravity abnormal value at the grid point (x, y), and gs (x, y) is the gravity measurement orderArranging the last sampling point value, and if the search strategy is traversed point by point, the total number S of the initial matching points0Comprises the following steps:
S0＝M2···(1)
based on the hierarchical neighborhood threshold search method, the total number S of initial matching points is estimated as follows:
firstly, when a larger grid interval number is used for horizontal and vertical coarse search, n is the grid interval number, and M is the grid number of each row, the search points selected in each row are g (x,1), g (x, n +1), g (x,2n +1), … …, g (x, n (k-1) +1) in sequence, wherein k is the serial number (positive integer) of each row of coarse search points.
The relationship between the grid number M of each line, the grid interval number n and the rough search point number k of each line is as follows:
n(k-1)+1≤M<nk+1···(2)
therefore, the number k of coarse search points per line can be obtained from equation (1):
since k is a positive integer, rounding k yields:
second, since the number of coarse search rows is the same as the number of search columns, the total number S of coarse search points within the search area mxm grid1Comprises the following steps:
thirdly, since there are k coarse search lines, each line has an optimal matching point, 8 neighborhood points (or 24 neighborhood points) around the optimal matching point are selected for matching, and the threshold is set to be (| g (x, y) -g) in the matching processs(x, y) is less than or equal to 4mGal), and the point selection standard of the matching points is improved. The number S of the additional search points around the 8-neighborhood (or 24-neighborhood) obtained by the formula (4)2Expression (c):
fourth, the total number of initial matching points S can be obtained from equations (5) and (6):
S＝S1+S2···(7)
wherein, due to M>>n, so that the estimated number S is substantially smaller than the number S of traversal searches in equation (1)0. Therefore, based on the hierarchical neighborhood threshold search method, the matching speed can be increased by improving the selection efficiency of the matching points, and the matching efficiency of the matching navigation is further improved.
And thirdly, judging and correcting the matching points in the step two by using a median filtering mismatching correction method. The inertial navigation error is usually obtained by taking the last time inertial navigation indication position as an initial value and then integrating the acceleration. If the matching time intervals are the same, the change of the correction number from the INS indicated position to the matching position between a plurality of adjacent matching points is small. Therefore, if the matching correction number of a certain adjacent point deviates greatly, the point matching result is not accurate, and the matching point estimation can be carried out by using median filtering.
And fourthly, repeating the steps II and III until the target area is reached.
The test verification result and the specific application of the inertia/gravity combined navigation method of the sky-sea integrated underwater vehicle are given below.
The gravity anomaly data and seafloor topography data for resolution 1 '× 1' in the present invention originated from the website of san Diego school, California university (http:// topex. ucsd. edu. /). According to the invention, the gravity anomaly data of the south China sea area are selected for research, and are compared and analyzed with the submarine topography data of the same area. The longitude and latitude range of the data is (longitude 112-116 degrees E, latitude 10-11 degrees N), wherein the maximum value of the gravity anomaly is 133.4mGal, the minimum value is-32.4 mGal, and the average value is 14.81 mGal. Since the subject group expects to invert the high-spatial-resolution marine gravity reference map by using 6-8 GNSS-R height measurement constellations, and further realize high-precision underwater gravity matching navigation, the invention converts data into grid resolution of 100 x 100m for verification through Matlab interpolation calculation.
Taking a two-dimensional/three-dimensional submarine topography reference map with the grid resolution of 100 × 100m in the research area and a two-dimensional/three-dimensional gravity anomaly reference map with the grid resolution of 100 × 100m in the research area as an example, the gravity fields in the east and the south are fluctuated severely, while the gravity fields in the northwest are changed more smoothly. Since the earth is not a density-balanced sphere, the imbalance in medium density results in a non-one-to-one correspondence between terrain and gravity values. The submarine topography features in the two-dimensional/three-dimensional submarine topography reference map are obvious, and the ridge angle mutation features such as a mountain peak, a ridge and a canyon are obvious; the gravity field in the two-dimensional/three-dimensional gravity anomaly reference diagram is distributed continuously, and the spatial distribution changes smoothly. Therefore, the hierarchical neighborhood threshold search method is more suitable for underwater gravity matching navigation research.
The hierarchical neighborhood threshold search method is characterized by being beneficial to improving the point selection standard of the matching points and accelerating the search matching speed, and being capable of improving the matching efficiency to a greater extent on the premise of ensuring the positioning accuracy of the Tercom algorithm. The invention is verified as follows: the above-described gravity anomaly reference map data is divided into 4 blocks from left to right, each block having a size of 1 ° × 1 °.
The gravity field characteristic parameters mainly comprise a gravity field standard deviation, a gradient standard deviation, a kurtosis coefficient, a roughness, an information entropy and the like. The standard deviation of the gravity field reflects the fluctuation change of the gravity field; the gravity field gradient standard deviation represents the change rate of the gravity field; the kurtosis coefficient is used for measuring the aggregation degree of data in the center and can reflect the sharp or flat degree of the top of the frequency distribution curve; the roughness reflects the average smoothness and local fluctuation of the gravity field in the whole area; the information entropy is used for evaluating the richness degree of the information quantity of the gravity area. The larger the standard deviation of the gravity field is, the larger the standard deviation of the gradient is, the larger the coarseness is, and the smaller the information entropy is, the richer the information is, and the better the matching is. According to the statistical information of the characteristics of the 4 regional gravity fields, the following information is obtained: firstly, the gravity field standard deviation, the gradient standard deviation and the roughness of the reference III are the largest, which shows that the area is more uneven and the gravity field changes more quickly; secondly, the information entropy of the reference III is minimum, which shows that the gravity anomaly characteristic information of the region is richer and more complex; finally, the kurtosis coefficient of the reference III is the minimum, because the kurtosis coefficient value under the condition of normal distribution is 3, if the kurtosis coefficient is more than 3, the observation quantity is more concentrated, and the tail part is shorter than the tail part of the normal distribution; when the kurtosis coefficient is less than 3, the observation quantity is not concentrated, and the matching is facilitated. Therefore, the selection criterion three-adaptability is better by integrating the judgment of each characteristic parameter.
The grid interval number, the neighborhood size and the threshold are all important influence factors of positioning precision and positioning time. The threshold may be 4 sigma (sigma is the standard deviation of the difference between the gravimeter measured data and the reference map data, and 4 sigma is about 99.99%). Because the influence of the grid spacing number and the neighborhood size on the positioning accuracy and the positioning time is opposite (the positioning accuracy is reduced and the positioning time is reduced along with the increase of the grid spacing number, the positioning accuracy is improved and the positioning time is increased along with the increase of the neighborhood), the invention carries out numerical simulation and analysis discussion on the optimization of the grid spacing number and the neighborhood size by the hierarchical neighborhood threshold search method from the aspects of the positioning accuracy and the positioning time. The Tercom numerical simulation parameter settings are as follows: the constant drift of the gyroscope is 0.01 degree/h, and the constant zero offset of the accelerometer is 10-3m/s2The inertial navigation root mean square error is subject to normal distribution, the navigational speed is 10m/s, the course north is 70 degrees, the initial position error is 0, the speed error is 0.03m/s, the course error is 0.05 degrees, the real-time measurement data of the gravimeter is random noise (the threshold value is 4mGal) with the standard deviation of 1mGal superposed on the sampling value of the real track in the gravity anomaly reference database, the number of sampling points is 220, and the sampling period is 20 s.
In order to verify the stability of the hierarchical neighborhood threshold search algorithm, the invention performs 80 times of calculation under the same condition, and performs numerical calculation on the positioning accuracy and efficiency under the condition that different grid intervals (3 grids, 4 grids, 5 grids, 6 grids) and neighborhoods (8 neighborhoods and 24 neighborhoods) are combined pairwise, as shown in fig. 3. Wherein, the left diagrams in fig. 3(a), 3(b), 3(c) and 3(d) represent the algorithm positioning accuracy under different grid spacing numbers and neighborhoods. As can be seen from FIG. 3, when the neighborhoods are the same, the average positioning accuracy of the algorithm is obviously reduced as the grid spacing number is increased; when the grid intervals are the same, the average positioning accuracy in the case of 24 neighborhoods is obviously higher than that in the case of 8 neighborhoods. The right hand graphs in fig. 3(a), 3(b), 3(c) and 3(d) represent algorithm localization times (localization time consists of reference graph loading, mapping, search matching, etc.) for different grid spacing numbers and neighborhoods. As can be seen from fig. 3, the conventional Tercom algorithm averages 31.79s for single positioning, wherein 20.04s for loading and drawing a reference map, 11.75s for searching matching, and each time is relatively dispersed; under the same condition, under 8 conditions that the interval number of grids (3 grids, 4 grids, 5 grids and 6 grids) and neighborhoods (8 neighborhoods and 24 neighborhoods) are combined pairwise, the algorithm is used for single positioning within 20-22 s, common time of loading, drawing and the like of a reference graph is eliminated, and the time for searching and matching is less than 2s, so that the matching efficiency is rapidly improved.
As can be seen from FIG. 4, based on 80 matching location calculations, the average location accuracy of the conventional Tercom algorithm is 87.94m, the standard deviation of the location accuracy is 50.66m, the location time is 31.79s, and the search matching time is 11.75 s. The research result shows that the importance of improving the spatial resolution of the marine gravity reference map on improving the underwater navigation precision is achieved, and if the subject group expects to complete the inversion of the gravity reference map with high spatial resolution by 6-8 GNSS-R height measurement satellite constellations, the underwater gravity matching navigation hundred-meter-level positioning precision is expected to be achieved. As can be seen from fig. 4(a) and 4(b), as the number of grid intervals increases, both the average positioning accuracy and the standard deviation of the positioning accuracy of the grid interval in the case of 8 neighborhoods have a significant decrease trend, and when 3 grid intervals are selected, the average positioning accuracy decreases by 0.99m and the standard deviation of the positioning accuracy decreases by 0.16 m; however, when 6 grid intervals were selected, the average positioning accuracy decreased by 74.09m, and the standard deviation of the positioning accuracy decreased by 30.45 m. In the case of the 24-neighborhood region, the average positioning accuracy and the standard deviation of the positioning accuracy are reduced, but the reduction range is small, and when the interval between the 3 grids and the 4 grids is selected, the average positioning accuracy (87.94m) and the standard deviation of the positioning accuracy (50.66m) are consistent with the results of the traditional Tercom algorithm; when 6 grid intervals are selected, the average positioning accuracy is reduced by only 8.61m, and the standard deviation of the positioning accuracy is reduced by 5.05 m. Therefore, as shown in fig. 4(c), in the case of the 24-neighborhood, the 3-grid and 4-grid spacing can both be selected to achieve 100% matching coincidence with the conventional Tercom algorithm, so that the positioning accuracy can be kept consistent. And (4) 80 times of matching location calculation, wherein the single average search matching time of the traditional Tercom algorithm is 11.75 s. As the grid interval number increases, the positioning time of the grid is in a decreasing trend, the positioning time under the condition of 8 neighborhoods is slightly better than that of 24 neighborhoods, but the positioning precision of the traditional Tercom algorithm cannot be kept when the 8 neighborhoods are selected, so that the 24 neighborhoods are selected. When 24 neighborhoods are selected, the 3-grid interval single search matching time is 1.40s, and the single search matching efficiency is improved by 8.48 times compared with that of the traditional Tercom algorithm under the same condition; however, the single search matching time of the 4-grid interval is 0.83s, which is 10.92s shorter than that of the traditional Tercom algorithm under the same condition, and the matching efficiency is improved by about 14.14 times.
Therefore, by setting the parameters of the embodiment as an example, compared with the traditional Tercom method, the hierarchical neighborhood threshold search method can effectively improve the matching efficiency by about 14.14 times on the basis of ensuring the positioning accuracy. According to the method, only the improvement condition of the short-time underwater navigation matching efficiency is calculated, and according to the accumulation characteristic of the INS error along with the time, if the running time is prolonged, the searching range of the INS error estimation is continuously increased; and meanwhile, the times of sampling points are increased, so that the searching and matching time of the traditional Tercom algorithm can be prolonged. Therefore, the hierarchical neighborhood threshold search method can shorten the matching time of a single search by at least 10.92 s.
In conclusion, the traditional Tercom algorithm is low in matching efficiency, so that a novel hierarchical neighborhood threshold search method is provided. The advantages are that: coarse search matching is conducted with a larger grid interval number, a threshold value is set, matching comparison is conducted on a plurality of neighborhood points around the selected optimal matching point of each coarse search line, a temporary optimal matching point in a range near each coarse search line is obtained, finally all the obtained temporary optimal matching points are compared, and then the optimal matching point in the whole search range is obtained.
Secondly, based on the novel hierarchical neighborhood threshold search method, by taking the parameter settings of the embodiment of the invention as an example, the result shows that: when the grid intervals are the same, the average positioning accuracy under the condition of 24 neighborhoods is obviously higher than that of 8 neighborhoods. When 3-mesh and 4-mesh intervals are selected, the average localization accuracy (87.94m) is consistent with that of the conventional Tercom algorithm. Secondly, when the neighborhoods are the same, the average positioning precision of the grid is in a reduction trend along with the increase of the grid interval number, and the reduction change rate under the condition of 8 neighborhoods is obviously greater than 24 neighborhoods. When an 8-neighborhood is selected, the 3-mesh inter-localization accuracy decreases by 0.99m, but the 6-mesh inter-localization accuracy decreases by 74.09 m. However, when 24 neighborhoods are selected, the positioning accuracy of both 3-mesh and 4-mesh intervals is not degraded, and the 6-mesh interval is only degraded by 8.61 m. And thirdly, when the 4 grids, the 24 neighborhoods and the 4mGal threshold parameters are adopted, on the premise of ensuring the positioning accuracy, the single search matching time is reduced from 11.75s to 0.83s, and the matching efficiency is improved by about 14.14 times. Therefore, the hierarchical neighborhood threshold search method is beneficial to improving the matching efficiency of the inertia/gravity combined navigation of the sky-sea integrated underwater vehicle.
In addition, the improvement of the spatial resolution of the global marine gravity reference map is a key factor for realizing high-precision underwater gravity matching navigation at present. Therefore, the numerical calculation of the invention takes the resolution data of a 100 x 100m grid as an example, and the average positioning precision is better than one hundred meters (87.94m) based on the traditional Tercom algorithm. The method provides theoretical support and method guarantee for designing 6-8 GNSS-R height measurement satellite constellations for the subject group in the future and further achieving hectometer-level underwater gravity matching navigation accuracy.
In an embodiment of the present invention, there is also disclosed a sky-sea integrated underwater vehicle inertia/gravity combined navigation system, including: the coarse searching module is used for performing coarse searching matching in a determined searching range by setting grid interval number to obtain a coarse searching optimal matching point; the screening module is used for carrying out matching comparison on a plurality of neighborhood points around the rough search optimal matching point according to a set threshold value to obtain rough search temporary optimal matching points corresponding to the rough search optimal matching points; the comparison module is used for comparing the temporary optimal matching points of each rough search to obtain the optimal matching points in the whole search range; and the output module is used for outputting the optimal matching point as a navigation result.
For the system embodiment, since it corresponds to the method embodiment, the description is relatively simple, and for the relevant points, refer to the description of the method embodiment section.
The embodiments in the present description are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The above description is only for the best mode of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
Claims (1)
1. A sky and sea integrated underwater vehicle inertia/gravity combined navigation method is characterized by comprising the following steps:
determining position information output by an INS at the current moment and an INS estimation error corresponding to the position information; determining a search range in a gravity reference diagram by taking the position indicated by the position information as a circle center and the multiple of the INS estimation error as a radius; wherein, the gravity benchmark picture includes: a row representing latitude and a column representing longitude;
selecting at least one rough search line within the determined search range according to a first set interval; according to a terrain contour matching method, performing coarse search on each coarse search line according to a second set interval to obtain an optimal matching point of each coarse search line;
according to a set threshold value, matching and comparing a plurality of neighborhood points around the optimal matching point of each coarse search line to obtain a temporary optimal matching point in the range near each coarse search line as a coarse search temporary optimal matching point;
comparing the temporary optimal matching points of the rough search to obtain the optimal matching points in the whole search range;
outputting the best matching point as a navigation result;
wherein, the matching search processAssuming that the search area size is an M × M grid, g (x, y) is an abnormal value of gravity at a grid point (x, y), gs (x, y) is the last sampling point value of the gravity measurement sequence, and the total number S of initial matching points is determined according to a point-by-point traversal search strategy0Comprises the following steps:
S0＝M2 (1)
based on a hierarchical neighborhood threshold search method, the total number S of initial matching points is estimated as follows:
firstly, when horizontal and vertical coarse searching is carried out according to the grid interval number n, the selected search points in each row are g (x,1), g (x, n +1), g (x,2n +1), … …, g (x, n (k-1) +1) in sequence, and k is the serial number of the coarse search points in each row; the relationship between the grid number M of each row, the grid interval number n and the coarse search point number k of each row is as follows:
n(k-1)+1≤M<nk+1 (2)
the number k of the coarse search points of each line is obtained by the formula (1):
k is a positive integer, and the integer of k is as follows:
second, since the number of coarse search rows is the same as the number of search columns, the total number S of coarse search points within the search area mxm grid1Comprises the following steps:
thirdly, because there are k coarse search lines in total, each line has an optimal matching point, 8 neighborhood points or 24 neighborhood points around the point are selected for matching, and the threshold value is set to be | g (x, y) -gs(x, y) | is less than or equal to 4mGal, and the number S of the peripheral additional search points is obtained when 8 neighborhood points or 24 neighborhood points can be obtained by the formula (4)2Expression (c):
fourth, the total number of initial matching points S can be obtained from equations (5) and (6):
S＝S1+S2 (7)
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| US11268813B2 (en) | 2020-01-13 | 2022-03-08 | Honeywell International Inc. | Integrated inertial gravitational anomaly navigation system |
| CN111735442A (en) * | 2020-06-17 | 2020-10-02 | 东南大学 | Underwater gravity passive navigation system |
| CN112229403B (en) * | 2020-08-31 | 2024-02-20 | 中国空间技术研究院 | Method for improving ocean gravity reconstruction accuracy based on three-dimensional correction principle of ground level |
| CN113686336A (en) * | 2021-06-30 | 2021-11-23 | 中国空间技术研究院 | Method for improving underwater navigation precision based on grid topological structure iteration optimal ring domain point |
| CN114136320B (en) * | 2021-11-19 | 2023-06-20 | 中国船舶重工集团公司第七0七研究所 | Ocean multi-physical-field parameter characteristic positioning fusion method based on characteristic complementation characteristics |
| CN114199238A (en) * | 2021-12-17 | 2022-03-18 | 中国空间技术研究院 | Method for improving underwater navigation efficiency and reliability based on soft interval local semicircle search |
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