CN108896040A - Sky sea integrated water diving device inertia/gravity Combinated navigation method and system - Google Patents

Abstract

The invention discloses a sky-sea integrated underwater vehicle inertial/gravity combined navigation method and system, wherein the method is based on the layered neighborhood threshold search principle to improve the matching efficiency of the underwater submersible's inertial/gravity combined navigation ; This method uses a large number of grid intervals for rough search matching, and sets a threshold and performs matching and comparison on several neighborhood points around the best matching point of each selected rough search line to obtain The temporary best matching point is finally compared with all the obtained temporary best matching points, and then the best matching point in the entire search range is obtained. This method has the advantages of fast matching efficiency, high positioning accuracy, and low computer performance requirements.

Description

Inertial/gravity integrated navigation method and system for sky-sea integrated underwater vehicle

technical field

The invention belongs to the field of aerospace and marine technology, and in particular relates to an inertial/gravity combined navigation method and system for a sky-sea integrated underwater submersible.

Background technique

Sky-sea integrated marine navigation (integrating multi-source information such as space-based, space-based, and sea-based) is the main trend in the development of underwater vehicle navigation systems. At present, the underwater vehicle navigation system is mainly an inertial navigation system (INS: Inertial Navigation System), which can provide favorable conditions for underwater vehicle navigation and weapon system precision strikes. However, the INS system has the disadvantage that errors accumulate over time, so external Correction. So far, the passive auxiliary navigation system composed of geophysical field and INS system has always been an international research hotspot to effectively suppress the accumulation of INS system errors. At present, the passive auxiliary navigation technology mainly includes: geomagnetic matching, terrain matching, gravity matching, etc. The geomagnetic field itself has long-term and short-term changes, so that the accuracy of the geomagnetic map cannot meet the high requirements, and the magnetic measurement technology has limitations such as magnetic interference. Terrain research has been carried out earlier, especially the terrain matching navigation technology on land has been applied to aircraft navigation, but the development of underwater terrain matching navigation is relatively late. Due to the need to emit sound waves outward, and the sonar survey topography cannot accurately detect deep-sea topography under complex sea conditions, the current underwater terrain matching navigation technology is only applicable to shallow sea areas. However, marine gravity matching navigation realizes navigation and positioning according to the gravity difference of different positions on the earth. It does not require underwater submersibles to surface or approach the water surface. It does not radiate energy when measuring, and the earth’s gravity field remains stable for a long time. Therefore, it is expected Realize accurate, autonomous and continuous long-endurance positioning of underwater vehicles.

The core problem of gravity matching technology is to solve key technologies such as high-precision and high-resolution gravity reference map, high-precision gravity measurement system, and gravity matching positioning algorithm. Since the last century, many scholars have proposed a variety of gravity matching algorithms, among which the Tercom algorithm is more popular. The advantage is that the calculation is simple and reliable, and the positioning accuracy is high; the disadvantage is that the search strategy of global traversal is adopted, the calculation amount is large, the calculation efficiency is low, and the requirements for the carrier track are high, and the error will increase sharply when there is a large deviation in the heading . Therefore, how to improve the positioning accuracy and matching efficiency of the Tercom algorithm is a research hotspot in the field of underwater navigation. Zhao Jianhu et al. introduced the matching criterion based on Hausdorff distance into the Tercom algorithm, and proposed the idea and algorithm of realizing the fine matching of the adaptation sequence by increasing the rotation change, adaptively determining the optimal rotation angle, and effectively improving the accuracy and reliability of matching navigation; Yan Li et al. carried out gravity matching simulation research based on the Tercom algorithm, and proved that the terrain roughness and slope variance have a strong correlation with the positioning accuracy of the Tercom algorithm. To sum up, at present, most scholars mainly conduct research on improving the accuracy of underwater navigation, while there are relatively few studies on improving the matching efficiency of underwater navigation.

Contents of the invention

The technical problem of the present invention is to overcome the deficiencies of the prior art, and provide a sky-sea integrated underwater vehicle inertial/gravity combined navigation method and system, aiming to overcome the shortcomings of the traditional Tercom algorithm, which has a relatively low search efficiency point by point, Improve the matching efficiency of the inertial/gravity integrated navigation of the sky-sea integrated underwater vehicle.

In order to solve the above technical problems, the present invention discloses an inertial/gravity combined navigation method for the Sky-Sea integrated underwater submersible, including:

Within the determined search range, the rough search matching is performed with the set grid interval number, and the best matching point of the rough search is obtained;

According to the set threshold, a number of neighborhood points around the rough search best match point are matched and compared to obtain the rough search temporary best match point corresponding to each rough search best match point;

Comparing each coarse search temporary best matching point to obtain the best matching point in the entire search range;

Outputting the best matching point as a navigation result.

In the above-mentioned inertial/gravity combined navigation method of the Sky-Sea integrated underwater submersible, it also includes:

Determine the location information output by the INS at the current moment, and the INS estimation error corresponding to the location information;

A search range is determined in the gravity reference map according to the position information and the INS estimation error corresponding to the position information.

In the sky-sea integrated underwater vehicle inertial/gravity integrated navigation method described above, the search range is determined in the gravity reference map according to the position information and the INS estimation error corresponding to the position information, including:

Taking the position indicated by the position information as the center of the circle, and taking the multiple of the INS estimation error as the radius, determine the search range in the gravity reference map.

In the sky-sea integrated underwater vehicle inertial/gravity combined navigation method, the gravity reference map includes: rows and columns; wherein, the rows represent latitude, and the columns represent longitude.

In the above-mentioned inertial/gravity combined navigation method of the sky-sea integrated underwater submersible, within the determined search range, the coarse search and matching are performed with the set grid interval number to obtain the best matching point of the coarse search, including:

Within the determined search range, select at least one coarse search line according to a first set interval;

According to the terrain contour matching method, the rough search is performed on each rough search line according to the second set interval, and the best matching point of each rough search line is obtained.

In the above inertial/gravity combined navigation method of the sky-sea integrated underwater submersible, according to the set threshold, a number of neighborhood points around the rough search best match point are matched and compared to obtain the corresponding rough search best match point. Coarse search of temporary best matching points, including:

According to the set threshold, match and compare several neighborhood points around the best matching point of each rough search line, and obtain the temporary best matching point within the vicinity of each rough search line as the rough search temporary best matching point.

Correspondingly, the present invention also discloses a sky-sea integrated underwater vehicle inertial/gravity combined navigation system, including:

The rough search module is used to perform rough search matching with the set grid interval number within the determined search range to obtain the best matching point for rough search;

The screening module is used to match and compare several neighborhood points around the rough search best match point according to the set threshold, and obtain the coarse search temporary best match point corresponding to each rough search best match point;

The comparison module is used to compare each rough search temporary best matching point to obtain the best matching point in the entire search range;

An output module, configured to output the best matching point as a navigation result.

The present invention has the following advantages:

1) The matching efficiency of underwater inertial/gravity combined navigation is fast;

2) Underwater gravity matching positioning accuracy is high;

3) High reliability of underwater gravity matching navigation;

4) The physical meaning of the underwater gravity matching process is clear.

Description of drawings

Fig. 1 is a flow chart of the steps of an inertial/gravity combined navigation method for a sky-sea integrated underwater submersible in an embodiment of the present invention;

Fig. 2 is a schematic flow chart of underwater inertial/gravity combined navigation in an embodiment of the present invention;

Fig. 3 is a schematic diagram of a comparison of algorithm positioning accuracy and efficiency under different grid interval numbers and neighborhoods in an embodiment of the present invention;

Fig. 4 is a schematic diagram of statistical information analysis of an algorithm under different grids and neighborhoods in an embodiment of the present invention;

Wherein, Fig. 3 (a) represents the number of 3 grid intervals, Fig. 3 (b) represents the number of 4 grid intervals, Fig. 3 (c) represents the number of 5 grid intervals, and Fig. 3 (d) represents the number of 6 grid intervals; Figure 4(a) shows the comparison of average positioning accuracy, Figure 4(b) shows the comparison of standard deviation of positioning accuracy, Figure 4(c) shows the comparison of matching coincidence rate, and Figure 4(d) shows the comparison of positioning time.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the embodiments disclosed in the present invention will be further described in detail below in conjunction with the accompanying drawings.

Referring to FIG. 1 , it shows a flow chart of the steps of an inertial/gravity integrated navigation method for a sky-sea integrated underwater submersible in an embodiment of the present invention. In this embodiment, the inertial/gravity combined navigation method of the sky-sea integrated underwater vehicle includes:

Step 101 , within the determined search range, perform rough search and matching with a set number of grid intervals to obtain the best matching point for rough search.

Step 102 , according to the set threshold, match and compare several neighborhood points around the rough search best match point to obtain rough search temporary best match points corresponding to each rough search best match point.

Step 103, comparing the rough search temporary best matching points to obtain the best matching points within the entire search range.

Step 104, outputting the best matching point as a navigation result.

In a preferred embodiment of the present invention, before step 101, the inertial/gravity integrated navigation method of the sky-sea integrated underwater vehicle may further include: determining the position information output by the INS at the current moment, and the position information Corresponding INS estimation error; according to the location information and the INS estimation error corresponding to the location information, determine the search range in the gravity reference map. Wherein, the gravity reference map includes: rows and columns; rows represent latitudes, and columns represent longitudes.

Preferably, the determining the 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: taking the position indicated by the position information as the center of the circle, taking the INS The multiple of the estimation error (such as 3 times the INS estimation error) is the radius, and the search range is determined in the gravity reference map.

Wherein, it should be noted that when determining the search range, the radius (the multiple of the INS estimation error) can be selected as an appropriate value according to the actual situation, which is not limited in this embodiment.

In a preferred embodiment of the present invention, the above step 101 may specifically include: within the determined search range, select at least one rough search line according to the first set interval; , perform a rough search according to the second set interval, and obtain the best matching point of each rough search line.

Appropriate values for the first set interval and the second set interval may be selected according to actual conditions, which is not limited in this embodiment.

In a preferred embodiment of the present invention, the above-mentioned step 102 may specifically include: according to the set threshold, perform matching and comparison on several neighborhood points around the best matching point of each rough search line, and obtain the vicinity range of each rough search line The inner temporary best matching point is used as a rough search temporary best matching point.

Based on the above-mentioned embodiment, the following will be described in conjunction with a specific example.

In this embodiment, the sky-sea integrated underwater vehicle inertial/gravity integrated navigation method is based on the layered neighborhood threshold search principle to improve the matching efficiency of the underwater vehicle inertial/gravity integrated navigation.

In this example, the research data comes from the global sea and land database (http://topex.ucsd.edu/) generated by joint aerospace, marine and other technologies. Latitude 10°～11°N), the resolution of the original seabed gravity anomaly is 1′×1′.

The underwater gravity matching accuracy is mainly determined by the matching algorithm and the characteristic parameters of the gravity field. Most of the existing gravity matching algorithms are derived from terrain matching algorithms. TERCOM (Terrain Contour Matching), ICCP (Iterative Closest Contour Point), and PDAF (Probability Data Associate Filtering) are all batch-related algorithms. SITAN (Sandia Inertial Terrain-Aided Navigation ) and PF (Particle Filter) algorithms are continuous matching algorithms based on Extended Kalman Filter (EKF) and direct probability criterion respectively.

The characteristic parameters of the gravity field are the key factors affecting the accuracy and matching probability of underwater navigation, mainly including: standard deviation of gravity field, standard deviation of slope, kurtosis coefficient, roughness, information entropy, correlation coefficient, etc. Based on the characteristics of continuous distribution of gravity field and slow change in spatial distribution, the present invention proposes a layered neighborhood threshold search method, aiming at improving the matching efficiency of the Tercom algorithm. The calculation principle and algorithm flow of the hierarchical neighborhood threshold search method are shown in Figure 2.

① Determine the coordinates of the starting position A and the target position B, and plan the course.

② After the submersible enters the matching area, when the gravity field measurement sequence is long enough, the hierarchical neighborhood threshold search method is used. First, estimate the radius of the search range according to the INS error at the current moment, and within the error search range of 3σ (the INS estimation error corresponding to the position information), perform coarse search matching with a larger grid to obtain the best match for each coarse search line secondly, select a number of neighborhood points around the best matching point of the coarse search line for matching comparison, and obtain the temporary best matching point within the vicinity of each rough search line, and set a threshold to improve the selection standard of matching points during the matching process; finally , compare and analyze several temporary best matching points obtained, and determine the best matching point within the search range.

In the matching search process, it is assumed that the size of the search area is an M×M grid, g(x,y) is the gravity anomaly at the grid point (x,y), and gs(x,y) is the last sample of the gravity measurement sequence Point value, if according to the point-by-point traversal search strategy, the total number of initial matching points S ₀ is:

S ₀ =M ² ···(1)

Based on the hierarchical neighborhood threshold search method, the total number of initial matching points S is estimated as follows:

First, when searching horizontally and vertically with a large number of grid intervals, let n be the number of grid intervals, and M be the number of grids in each row, then the search points selected for each row are g(x,1) , g(x,n+1), g(x,2n+1),...,g(x,n(k-1)+1), where k is the serial number of the rough search point in each row (positive integer).

The relationship between the number of grids M in each row, the number of grid intervals n, and the number of coarse search points k in each row is as follows:

n(k-1)+1≤M<nk+1···(2)

Therefore, according to the formula (1), the rough search points k of each row can be obtained:

Since k is a positive integer, it can be obtained by rounding k to an integer:

Second, since the number of coarse search rows and search columns is the same, the total number of coarse search points S1 in the search area M _× M grid is:

Third, since there are k rough search lines in total, and each line has a best matching point, 8 neighborhood points (or 24 neighborhood points) around this point are selected for matching, and the threshold is set to (|g( x,y)-g _s (x,y)|≤4mGal), which improves the selection standard of matching points. From the formula (4), the expression of the number of additional search points S ₂ around when there are 8 neighborhoods (or 24 neighborhoods) can be obtained:

Fourth, the total number of initial matching points S can be obtained from formulas (5) and (6):

S＝S ₁ +S ₂ ···(7)

Wherein, since M>>n, the estimated number S is significantly smaller than the traversal search number S ₀ in formula (1). Therefore, based on the hierarchical neighborhood threshold search method, the matching speed can be accelerated by improving the selection efficiency of matching points, thereby improving the matching efficiency of matching navigation.

③Use the median filtering error matching correction method to judge and correct the matching points in step ②. The current inertial navigation error is usually obtained from the position indicated by the inertial navigation at the previous time as the initial value, and then the acceleration is integrated. If the matching time interval is the same, the number of corrections from each indication position of the INS to the matching position between several adjacent matching points is small. Therefore, if the matching correction number of a certain adjacent point deviates greatly, the matching result of this point is inaccurate, and the matching point can be estimated by median filtering.

④Repeat steps ② and ③ until reaching the target area.

The experimental verification results and specific applications of the inertial/gravity integrated navigation method for the Sky-Sea integrated underwater vehicle are given below.

The gravity anomaly data and seabed topography data with a resolution of 1′×1′ in the present invention come from the website of the University of California, San Diego (http://topex.ucsd.edu/). The present invention selects the gravity anomaly data in the South China Sea area for research, and compares and analyzes it with the seabed topography data in the same area. The range of latitude and longitude of the data is (longitude 112°～116°E, latitude 10°～11°N), where the maximum value of the gravity anomaly is 133.4mGal, the minimum value is -32.4mGal, and the average value is 14.81mGal. Since the research group expects to use 6 to 8 GNSS-R altimetry constellations to invert the high spatial resolution marine gravity reference map, and then realize high-precision underwater gravity matching navigation, the present invention converts the data into a grid through Matlab interpolation calculation. The network resolution is 100×100m for verification.

Taking the 2D/3D submarine topographic reference map with a grid resolution of 100×100m in the study area and the 2D/3D gravity anomaly reference map with a grid resolution of 100×100m in the study area as examples, the gravity field in the east and southeast fluctuates violently. While the gravity field in the northwest changes more smoothly. Because the earth is not a sphere with uniform density, the unevenness of medium density leads to a non-one-to-one correspondence between terrain and gravity. In the 2D/3D seabed topography reference map, the seabed topographic features are prominent, and the sharp edges and corners such as peaks, ridges, and canyons have obvious abrupt changes; the gravity field in the 2D/3D gravity anomaly reference map is continuously distributed, and the spatial distribution changes smoothly. Therefore, the hierarchical neighborhood threshold search method is more suitable for underwater gravity matching navigation research.

The feature of the hierarchical neighborhood threshold search method is that it is beneficial to improve the selection criteria of matching points to speed up the search and matching speed, and can greatly improve the matching efficiency under the premise of ensuring the positioning accuracy of the Tercom algorithm. The verification of the present invention is as follows: divide the data of the gravity anomaly reference map into 4 blocks from left to right, each block is 1°×1° in size.

Gravity field characteristic parameters mainly include gravity field standard deviation, slope standard deviation, kurtosis coefficient, roughness, information entropy, etc. The standard deviation of the gravity field reflects the ups and downs of the gravity field; the standard deviation of the gradient of the gravity field indicates the rate of change of the gravity field; the kurtosis coefficient is used to measure the degree of data aggregation in the center, which can reflect the sharpness or flatness of the top of the frequency distribution curve; Roughness reflects the average smoothness and local undulations of the gravity field in the entire region; information entropy is used to evaluate the richness of information in the gravity region. The larger the standard deviation of the gravity field, the larger the slope standard deviation, the larger the roughness, and the smaller the information entropy, the richer the information and the more conducive to matching. According to the statistical information of the gravity field characteristics of the four regions: First, the standard deviation of the gravity field, the standard deviation of the slope and the roughness of the benchmark three are the largest, indicating that this area is more uneven and the gravity field changes faster; second, the information entropy of the benchmark three is the smallest, indicating that the characteristic information of the gravity anomaly in this area is richer and more complex; finally, the kurtosis coefficient of benchmark 3 is the smallest, because the kurtosis coefficient value in the case of normal distribution is 3, if the kurtosis coefficient is greater than 3, it means that the observed quantity It is more concentrated and has a shorter tail than the normal distribution; when the kurtosis coefficient is less than 3, it means that the observations are less concentrated and more conducive to matching. Therefore, based on the judgment of various characteristic parameters, the selection criterion three has better adaptability.

The number of grid intervals, the size of the neighborhood, and the threshold are all important factors affecting the positioning accuracy and positioning time. The threshold value can be 4σ (σ is the standard deviation of the difference between the measured data of the gravimeter and the data of the reference map, and 4σ is about 99.99%). Since the number of grid intervals and the size of the neighborhood have opposite effects on the positioning accuracy and positioning time (as the number of grid intervals increases, the positioning accuracy will decrease, and the positioning time will decrease; and as the neighborhood increases, the positioning accuracy will decrease increase, the positioning time will increase), so from the perspective of positioning accuracy and positioning time, the present invention performs numerical simulation, analysis and discussion on the optimization of the number of grid intervals and the size of the neighborhood in the hierarchical neighborhood threshold search method. Tercom numerical simulation parameters are set as follows: gyroscope constant value drift 0.01°/h, accelerometer constant value zero deviation 10 ^-3 m/s ² (RMS error of inertial navigation obeys normal distribution), speed 10m/s, heading north 70° east, initial position error 0, velocity error 0.03m/s, heading error 0.05°, the real-time measurement data of the gravimeter is random noise with a standard deviation of 1mGal superimposed on the sampling value of the real track in the gravity anomaly reference database (threshold Take it as 4mGal), the number of sampling points is 220, and the sampling period is 20s.

In order to verify the stability of the hierarchical neighborhood threshold search algorithm, the present invention has carried out 80 calculations under the same conditions, with different grid intervals (3 grids, 4 grids, 5 grids, 6 grids, etc.) respectively. Grids) and neighborhoods (8 neighborhoods, 24 neighborhoods) are combined in pairs to numerically calculate the positioning accuracy and efficiency, as shown in Figure 3. Among them, the left graphs in Figures 3(a), 3(b), 3(c) and 3(d) show the positioning accuracy of the algorithm under different grid intervals and neighborhoods. It can be seen from Figure 3 that when the neighborhoods are the same, the average positioning accuracy of the algorithm decreases significantly as the number of grid intervals increases; when the number of grid intervals is the same, the average positioning accuracy of the 24-neighborhood case is significantly higher than that of the 8-neighborhood . The right graphs in Figures 3(a), 3(b), 3(c) and 3(d) show the positioning time of the algorithm under different grid intervals and neighborhoods (the positioning time is determined by loading, drawing, searching matching time components). According to Fig. 3, it can be seen that the traditional Tercom algorithm takes an average of 31.79s for a single positioning, of which 20.04s is used for loading and drawing the reference map, and 11.75s is used for searching and matching, and each time is scattered; and under the same conditions, the proposed method of the present invention Algorithm single positioning in 8 cases of pairwise combinations of different grid intervals (3 grids, 4 grids, 5 grids, 6 grids) and neighborhoods (8 neighborhoods, 24 neighborhoods) If the time is between 20 and 22s, it means that the search and matching time is less than 2s after removing the common time such as loading and drawing the reference map, so the matching efficiency is rapidly improved.

As can be seen from Figure 4, based on 80 matching positioning calculations, the average positioning accuracy of the traditional Tercom algorithm is 87.94m, the standard deviation of positioning accuracy is 50.66m, the positioning time is 31.79s, and the search and matching time is 11.75s. The above research results show the importance of improving the spatial resolution of the marine gravity reference map to improve the accuracy of underwater navigation. , it is expected to achieve 100-meter-level positioning accuracy of underwater gravity matching navigation. According to Figure 4(a) and 4(b), as the number of grid intervals increases, in the case of 8 neighborhoods, the average positioning accuracy and the standard deviation of positioning accuracy have a tendency to decrease significantly. When 3 grid intervals are selected, The average positioning accuracy is reduced by 0.99m, and the standard deviation of positioning accuracy is reduced by 0.16m; but when the 6-grid interval is selected, the average positioning accuracy is reduced by 74.09m, and the standard deviation of positioning accuracy is reduced by 30.45m. However, in the case of 24 neighborhoods, although the average positioning accuracy and the standard deviation of positioning accuracy also have a downward trend, the reduction is relatively small. When the interval of 3 grids and 4 grids is selected, the average positioning accuracy (87.94m) And the standard deviation of positioning accuracy (50.66m) is consistent with the results of the traditional Tercom algorithm; when the 6-grid interval is selected, the average positioning accuracy is only reduced by 8.61m, and the standard deviation of positioning accuracy is reduced by 5.05m. Therefore, as shown in Figure 4(c), in the case of 24 neighborhoods, choosing 3-grid and 4-grid intervals can achieve a 100% matching coincidence rate with the traditional Tercom algorithm, which can keep the positioning accuracy consistent. 80 matching positioning calculations, the average single search and matching time of the traditional Tercom algorithm is 11.75s. As the number of grid intervals increases, the positioning time tends to decrease, and the positioning time in the case of 8 neighborhoods is slightly better than that of 24 neighborhoods, but the positioning accuracy of the traditional Tercom algorithm cannot be maintained when the 8 neighborhoods are selected, so the 24 neighborhoods are selected. area. When 24 neighborhoods are selected, the single search matching time of 3 grid intervals is 1.40s, which is 8.48 times higher than the single search matching efficiency of the traditional Tercom algorithm under the same conditions; however, the single search matching time of 4 grid intervals is 0.83s , which is 10.92s shorter than the single search matching time of the traditional Tercom algorithm under the same conditions, and the matching efficiency is increased by about 14.14 times.

It can be seen that, taking the parameter setting of this embodiment as an example, compared with the traditional Tercom method, the layered neighborhood threshold search method of the present invention can effectively improve the matching efficiency by about 14.14 times on the basis of ensuring the positioning accuracy. The present invention only calculates the short-term improvement of underwater navigation matching efficiency. According to the characteristics of INS error accumulation over time, if the running time is prolonged, the search range of INS error estimation will continue to increase; at the same time, the number of sampling points increases, which can increase the traditional Search matching time for the Tercom algorithm. Therefore, the hierarchical neighborhood threshold search method can shorten the matching time of a single search by at least 10.92s.

To sum up, due to the low matching efficiency of the traditional Tercom algorithm, a novel hierarchical neighborhood threshold search method is proposed. Advantages: Rough search matching is performed with a large number of grid intervals, and a threshold is set and a number of neighborhood points around the selected best matching point of each rough search row are matched and compared to obtain temporary data within the vicinity of each rough search row. The best matching point is finally compared with all the obtained temporary best matching points, and then the best matching point in the entire search range is obtained.

Secondly, based on the new hierarchical neighborhood threshold search method, taking the parameter setting of the embodiment of the present invention as an example, the results show that: ① When the number of grid intervals is the same, the average positioning accuracy in the case of 24 neighborhoods is significantly higher than that of 8 neighborhoods. When 3-grid and 4-grid intervals are selected, the average positioning accuracy (87.94m) is consistent with the positioning accuracy of the traditional Tercom algorithm. ②When the neighborhoods are the same, as the number of grid intervals increases, the average positioning accuracy tends to decrease, and the rate of change in the case of 8 neighborhoods is significantly greater than that of 24 neighborhoods. When the 8-neighborhood is selected, the positioning accuracy of the 3-grid interval is reduced by 0.99m, but the positioning accuracy of the 6-grid interval is reduced by 74.09m. However, when 24 neighborhoods are selected, neither the 3-grid nor 4-grid interval positioning accuracy is reduced, and the 6-grid interval is only reduced by 8.61m. ③ When using 4 grids, 24 neighborhoods and 4mGal threshold parameters, under the premise of ensuring the positioning accuracy, the single search and matching time is reduced from 11.75s to 0.83s, and the matching efficiency is increased by about 14.14 times. Therefore, the hierarchical neighborhood threshold search method is beneficial to improve the matching efficiency of the inertial/gravity integrated navigation of the sky-sea integrated underwater vehicle.

In addition, the improvement of the spatial resolution of the global ocean gravity reference map is a key factor for realizing high-precision underwater gravity matching navigation. Therefore, the numerical calculation of the present invention takes 100×100m grid resolution data as an example, and the average positioning accuracy obtained based on the traditional Tercom algorithm is better than 100 meters (87.94m). It provides theoretical support and method guarantee for the research group to design 6-8 GNSS-R altimetry satellite constellations in the future, and then realize the 100-meter-level underwater gravity matching navigation accuracy.

In the embodiment of the present invention, a sky-sea integrated underwater submersible inertial/gravity integrated navigation system is also disclosed, including: a rough search module, which is used to perform a search at a set grid interval within a determined search range. Coarse search matching to obtain the rough search best matching point; the screening module is used to match and compare several neighborhood points around the rough search best matching point according to the set threshold, and obtain the rough search corresponding to each rough search best matching point a temporary best matching point; a comparison module, which compares each rough search temporary best matching point to obtain the best matching point in the entire search range; an output module, which is used to output the best matching point as a navigation result.

As for the system embodiment, since it corresponds to the method embodiment, the description is relatively simple, and for the related parts, please refer to the description of the method embodiment.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other.

The above description is only the best specific implementation mode of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of changes or modifications within the technical scope disclosed in the present invention. Replacement should be covered within the protection scope of the present invention.

The content that is not described in detail in the specification of the present invention belongs to the well-known technology of those skilled in the art.

Claims (7)

1. a kind of sky sea integrated water diving device inertia/gravity Combinated navigation method, which is characterized in that including：

In determining search range, coarse search matching is carried out to set grid interval number, obtains coarse search optimal match point；

According to setting threshold value, matching comparison is carried out to several neighborhood points around coarse search optimal match point, obtains each coarse search The interim optimal match point of coarse search corresponding to optimal match point；

The interim optimal match point of each coarse search is compared, the optimal match point in entire search range is obtained；

It is exported the optimal match point as navigation results.

2. integrated water diving device inertia/gravity Combinated navigation method in sky sea according to claim 1, feature exist In further including：

Determine the location information and the corresponding INS evaluated error of the location information of current time INS output；

The corresponding INS evaluated error with the location information according to the positional information determines search model in gravity datum figure It encloses.

3. integrated water diving device inertia/gravity Combinated navigation method in sky sea according to claim 2, feature exist In the corresponding INS evaluated error with the location information according to the positional information determines search in gravity datum figure Range, including：

Using position indicated by the location information as the center of circle, using the multiple of the INS evaluated error as radius, in gravity datum Search range is determined in figure.

4. integrated water diving device inertia/gravity Combinated navigation method in sky sea according to claim 3, feature exist In, the gravity datum figure, including：Row and column；Wherein, row indicates that latitude, column indicate longitude.

5. integrated water diving device inertia/gravity Combinated navigation method in sky sea according to claim 4, feature exist In, it is described in determining search range, coarse search matching is carried out to set grid interval number, obtains coarse search best match Point, including：

In determining search range, it is spaced according to the first setting, selects at least one coarse search row；

According to terrain contour matching method, to each coarse search row, coarse search is carried out according to the second setting interval, obtains each coarse search row Optimal match point.

6. integrated water diving device inertia/gravity Combinated navigation method in sky sea according to claim 5, feature exist In, it is described according to setting threshold value, matching comparison is carried out to several neighborhood points around coarse search optimal match point, acquisition is each slightly to search The interim optimal match point of coarse search corresponding to rope optimal match point, including：

According to setting threshold value, matching comparison is carried out to several neighborhood points around the optimal match point of each coarse search row, is obtained every Interim optimal match point in a coarse search row environs, as the interim optimal match point of coarse search.

7. a kind of sky sea integrated water diving device inertia/gravity integrated navigation system, which is characterized in that including：

Coarse search module, for carrying out coarse search matching to set grid interval number, slightly being searched in determining search range Rope optimal match point；

Screening module, for carrying out matching comparison to several neighborhood points around coarse search optimal match point according to setting threshold value, Obtain the interim optimal match point of coarse search corresponding to each coarse search optimal match point；

Comparison module is compared the interim optimal match point of each coarse search, obtains the optimal match point in entire search range；

Output module, for being exported the optimal match point as navigation results.