Method and system for group optimization underwater gravity matching navigation

Abstract

The invention discloses a group optimization underwater gravity matching navigation method and system, and relates to the technical field of underwater navigation positioning. The method comprises the following steps: acquiring navigation data; the navigation data comprises inertial navigation position data and a gravity sequence; the gravity sequence comprises a set number of underwater gravity navigation data; according to the gravity sequence and a set reference map, carrying out data matching by adopting a dogvessel colony algorithm to obtain a matching result; performing position correction on the inertial navigation data according to a matching result to obtain corrected position data; the corrected position data is used for carrying out track navigation on the navigation system. According to the invention, underwater gravity matching navigation can be realized and the navigation precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater navigation and positioning, and in particular to a method and system for group-optimized underwater gravity matching navigation. Background Art

[0002] The Inertial Navigation System (INS) can provide real-time information on the space position, speed, attitude, etc. of the carrier. It is autonomous, all-weather, and concealed, and is the main navigation equipment for underwater operation platforms. Since the inertial navigation uses an integral method to process the output data, the system error accumulates and diverges over time. In severe cases, it will deviate too much from the established route, which is very likely to cause safety accidents. To improve accuracy, inertial navigation systems are usually combined with other types of sensors. For example, when operating on land and on the surface, the inertial navigation system is combined with the Global Navigation Satellite System (GNSS), the Compass Navigation Satellite System (CNS), the charge-coupled device (CCD) star sensor, etc.; when operating underwater, the inertial navigation system is combined with Doppler Velocity Log (DVL), ultra-short baseline (USBL, short baseline, SBL, long baseline, LBL), gravity / topography / geomagnetic measurement equipment, dead reckoning system (DR) and other terminals.

[0003] The Earth's gravity field is a comprehensive reflection of the spatial distribution of Earth's matter. As an important component of the Earth's gravity field, the ocean gravity field is a basic geographic information guarantee in the fields of seabed resource development, marine infrastructure construction, national security, etc., and has important scientific significance and application value. With the development of observation technology and the accumulation of observation data, the ability to obtain information and apply services in the ocean gravity field has been greatly improved. The underwater gravity matching navigation system composed of an inertial navigation system and a gravity measurement terminal does not require external transmission and reception of signals, is not affected by the surrounding environment and is not interfered by external signals, and can meet the real-time, continuous, concealed, and accurate autonomous navigation requirements of underwater submersibles. Intelligent algorithms can improve their own performance by simulating or realizing human learning behavior and reorganizing existing knowledge structures. After decades of development, intelligent algorithms have spanned multiple disciplines such as computer science, pattern recognition, and statistics, and have made great technological breakthroughs in aerospace, image recognition, weather forecasting, transportation, and smart city construction. As the core theory of artificial intelligence, the way intelligent algorithms solve problems can be summarized as follows: ① sensor data collection; ② preprocessing and feature extraction; ③ reasoning, prediction, optimization, and classification recognition. Underwater gravity matching navigation can be regarded as an optimization problem. The role of intelligent algorithms is to provide reference and optimization ideas for key technical links involved in underwater gravity matching navigation, such as navigation path planning, gravity matching algorithm implementation, and algorithm reliability evaluation.

[0004] However, what method is used to achieve underwater gravity matching navigation is crucial. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for group-optimized underwater gravity matching navigation, so as to realize underwater gravity matching navigation and improve navigation accuracy.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for group-optimized underwater gravity matching navigation, the method being applied to a navigation system; the navigation system comprising: an inertial navigation system and a gravity measuring instrument terminal; the inertial navigation system and the gravity measuring instrument terminal being connected; the inertial navigation system being used to measure inertial navigation position data; the gravity measuring instrument terminal being used to collect underwater gravity navigation data;

[0008] The method comprises:

[0009] Acquire navigation data; the navigation data includes: the inertial navigation position data and the gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data;

[0010] According to the gravity sequence and the set reference map, a salp group algorithm is used to perform data matching to obtain a matching result;

[0011] The inertial navigation data is position-corrected according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

[0012] Optionally, data matching is performed using a salp group algorithm according to the gravity sequence and a set reference map to obtain a matching result, specifically including:

[0013] Determine parameter data of a salp group; the parameter data include: population size, number of iterations, spatial dimension and acceleration factor; the salp group is a group consisting of a set number of underwater gravity navigation data in a gravity sequence; the value of the population size is equal to the value of the set number;

[0014] Determining the fitness function of the salp group; the fitness function is determined according to the degree of agreement between the gravity anomaly profile of the gravity measuring instrument terminal and the underwater gravity navigation data;

[0015] Within the range of the spatial dimension, performing translation processing on the salp population according to the population scale to obtain a processed salp population;

[0016] Performing a spatial search in the processed salp population according to the population size and the spatial dimension to obtain a cut sequence, and determining an initial leader position and an initial follower position according to the cut sequence;

[0017] The fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain a matching result.

[0018] Optionally, the fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain a matching result, specifically including:

[0019] Using the fitness function on the set reference graph, the initial leader position and the initial follower position are iteratively updated according to the acceleration factor to obtain an updated leader position and an updated follower position; the updated leader position and the updated follower position are both within the range of the spatial dimension;

[0020] Determine a matching result based on the updated leader position and the updated follower position;

[0021] The nth iteration process is:

[0022] Calculating a fitness value at the nth iteration according to the fitness function, the leader position at the nth iteration, and the follower position at the nth iteration;

[0023] Determine whether an iteration stop condition is met; the stop condition is: the number of n-th iterations is equal to the number of iterations or the fitness value under the n-th iteration is equal to the fitness threshold;

[0024] If yes, the leader position at the nth iteration and the follower position at the nth iteration are output as the matching result;

[0025] If not, the acceleration factor is updated, and the leader position at the n+1th iteration and the follower position at the n+1th iteration are determined.

[0026] Optionally, the fitness function is expressed as:

[0027]

[0028] Wherein, MSD(·) is the fitness function; x is the x-axis component of the track position coordinates of the underwater gravity navigation data; y is the y-axis component of the track position coordinates of the underwater gravity navigation data; tx is the horizontal coordinate value of the track deviation; ty is the vertical coordinate value of the track deviation; Δg i (x, y) is underwater gravity navigation data; is the gravity anomaly profile; N is the population size; i is the serial number of the underwater gravity navigation data in the population.

[0029] Optionally, the update expression of the acceleration factor is:

[0030]

[0031] Among them, c1 is the acceleration factor; L is the total number of iterations, and l is the current number of iterations.

[0032] A system for group-optimized underwater gravity matching navigation, the system comprising:

[0033] A data acquisition module, used to acquire navigation data; the navigation data includes: inertial navigation position data and gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data;

[0034] A matching module, used for performing data matching using a salp group algorithm according to the gravity sequence and a set reference map to obtain a matching result;

[0035] The correction module is used to perform position correction on the inertial navigation data according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

[0036] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned group-optimized underwater gravity matching navigation method.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned group-optimized underwater gravity matching navigation method.

[0038] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] The present invention provides a method and system for group-optimized underwater gravity matching navigation, which obtains navigation data; the navigation data includes: inertial navigation position data and gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data; according to the gravity sequence and a set reference map, a salp group algorithm is used to match the data to obtain a matching result; according to the matching result, the position of the inertial navigation data is corrected to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system; and because the salp group algorithm is used for data matching, underwater gravity matching navigation can be realized and navigation accuracy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 A schematic diagram of a salp chain provided by an embodiment of the present invention;

[0042] Figure 2 A flow chart of a method for group-optimized underwater gravity matching navigation provided by an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the actual track position and the inertial navigation output position offset provided by an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of a salp group formed after the track position is initialized according to an embodiment of the present invention;

[0045] Figure 5 A calculation flow chart of a salp swarm algorithm provided in an embodiment of the present invention;

[0046] Figure 6 A schematic diagram of the difference in longitude and latitude of Route 2 provided by an embodiment of the present invention under the salp swarm algorithm and the real track;

[0047] Figure 7 A curve diagram showing the difference in longitude and latitude between route 2 provided in an embodiment of the present invention under the salp swarm algorithm and the real track;

[0048] Figure 8 A schematic diagram of the difference in longitude and latitude of Route 1 provided in an embodiment of the present invention under the salp swarm algorithm and the real track;

[0049] Fig. 9 A curve diagram showing the difference in longitude and latitude between route 2 provided in an embodiment of the present invention under the salp swarm algorithm and the real track;

[0050] Fig.10 A structural diagram of a system for group-optimized underwater gravity matching navigation provided by an embodiment of the present invention.

[0051] Explanation of symbols:

[0052] Data acquisition module-1, matching module-2, correction module-3. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Salps are marine organisms that are highly similar in shape to jellyfish, being cylindrical in shape with an opening at the end that allows them to pump water into their gelatinous bodies to move around. Marine organisms share some common grouping behaviors, such as in fish, which is called a school, and in salps, which is called a salp chain, such as Figure 1 As shown in the figure, the salp swarm algorithm can effectively solve the underwater gravity matching navigation under nonlinear conditions; it can adaptively select the optimal matching result from many schemes; the algorithm is simple and feasible, and is easy to promote and apply.

[0055] The purpose of the present invention is to provide a method and system for group-optimized underwater gravity matching navigation, so as to realize underwater gravity matching navigation and improve navigation accuracy.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] The embodiment of the present invention provides a method for group-optimized underwater gravity matching navigation, which is applied to a navigation system; the navigation system comprises: an inertial navigation system and a gravity meter terminal; the inertial navigation system and the gravity meter terminal are connected; the inertial navigation system is used to measure inertial navigation position data; and the gravity meter terminal is used to collect underwater gravity navigation data.

[0059] like Figure 2 As shown, the method comprises:

[0060] Step 100: Obtain navigation data; the navigation data includes: inertial navigation position data and gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data.

[0061] Step 200: Perform data matching using the salp swarm algorithm according to the gravity sequence and the set reference map to obtain a matching result.

[0062] Among them, the salp group algorithm is used to match the data according to the gravity sequence and the set reference map to obtain the matching results, which specifically include:

[0063] Determine the parameter data of the salp group; the parameter data include: population size, number of iterations, spatial dimension and acceleration factor; the salp group is a group composed of a set number of underwater gravity navigation data in the gravity sequence; the value of the population size is equal to the value of the set number.

[0064] The fitness function of the salp group is determined; the fitness function is determined based on the degree of agreement between the gravity anomaly profile of the gravity meter terminal and the underwater gravity navigation data.

[0065] Within the range of spatial dimension, the salp population is translated according to the population size to obtain the processed salp population.

[0066] According to the population size, a spatial search is performed in the processed salps according to the spatial dimension to obtain a cut sequence, and the initial leader position and the initial follower position are determined according to the cut sequence.

[0067] The fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain the matching result.

[0068] Specifically, the expression of the fitness function is:

[0069]

[0070] Wherein, MSD(·) is the fitness function; x is the x-axis component of the track position coordinates of the underwater gravity navigation data; y is the y-axis component of the track position coordinates of the underwater gravity navigation data; tx is the horizontal coordinate value of the track deviation; ty is the vertical coordinate value of the track deviation; Δg i (x, y) is underwater gravity navigation data; is the gravity anomaly profile; N is the population size; i is the serial number of the underwater gravity navigation data in the population.

[0071] Wherein, a fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain a matching result, specifically including:

[0072] The fitness function is used to iteratively update the initial leader position and the initial follower position on the set reference graph according to the acceleration factor to obtain an updated leader position and an updated follower position; the updated leader position and the updated follower position are both within the range of the spatial dimension.

[0073] The matching result is determined based on the updated leader position and the updated follower position.

[0074] The nth iteration process is:

[0075] According to the fitness function, the leader position at the nth iteration and the follower position at the nth iteration, the fitness value at the nth iteration is calculated.

[0076] Determine whether the iteration stop condition is met; the stop condition is: the number of n-th iterations is equal to the number of iterations or the fitness value under the n-th iteration is equal to the fitness threshold.

[0077] If so, the leader position at the nth iteration and the follower position at the nth iteration are output as the matching result.

[0078] If not, the acceleration factor is updated, and the leader position at the n+1th iteration and the follower position at the n+1th iteration are determined.

[0079] The update expression of the acceleration factor is:

[0080]

[0081] Among them, c1 is the acceleration factor; L is the total number of iterations, and l is the current number of iterations.

[0082] Step 300: Perform position correction on the inertial navigation data according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

[0083] In practical applications, the operation process of the group optimization underwater gravity matching navigation method provided by the present invention can also be as follows:

[0084] 1) Data preprocessing.

[0085] Run the data processing software, create a new project, import the collected inertial navigation position data, gravity anomaly reference map, and sea surface space gravity anomaly sequence output by the underwater gravity acquisition terminal, hereinafter referred to as "inertial navigation data", "reference map", and "gravity sequence" respectively.

[0086] Underwater gravity matching navigation is an auxiliary inertial navigation method. When the inertial navigation data has errors due to the time accumulation effect, the ocean gravimeter is used to obtain the gravity sequence value, and the gravity sequence value is aligned with the reference map that has been obtained a priori to obtain the real-time position, so as to correct the erroneous inertial navigation data and achieve the purpose of precise navigation.

[0087] (2) Initialize the salp swarm. For details on the calculation process of the salp swarm algorithm, see Figure 5 .

[0088] ① Determine the calculation method. The registration operation of gravity sequence values ​​is usually presented in the form of lines or surfaces. The present invention focuses on the line method, that is, the calculation point for a registration operation is set as at the same time It is also the first point of the line registration, such as Figure 3 shown.

[0089] ② Set the parameters of the salp swarm algorithm. Let ub be the upper bound of the search space, lb be the lower bound of the search space, N be the size of the salp swarm, and d be the dimension of the space.

[0090] ③ Initialize the salp group. Perform N translation operations on the offset track within the d-dimensional search space. Assume that in two-dimensional space, its position is recorded in the matrix X i middle, Figure 4 The salp group formed by the translation of the offset track is shown. Since the line expression is not convenient for storage and calculation, the As representatives of different groups of ascidian, they are recorded in the matrix X i In, as follows:

[0091]

[0092] (3) Set the fitness function.

[0093] The registration operation of gravity sequence values ​​on the reference map obtained a priori is inseparable from the calculation of the fitness function. The fitness function reflects the degree of consistency between the gravity anomaly profile and the data collected by the gravimeter. The MSD operator is selected as the fitness function.

[0094] (4) Iterative calculation.

[0095] ①Iterate the calculation from the first point of the gravity sequence until the last point.

[0096] ② Starting from the current calculation point, intercept this point and the following seven points (hereinafter referred to as the "intercepted sequence") to form a gravity sequence including eight points, such as Figure 3 At the same time, the real-time position data with deviation collected by the inertial navigation system is extracted to facilitate substitution into the fitness function for calculation;

[0097] ③ Take the current calculation point as the center and select the surrounding 3 nautical miles as the candidate area. According to the parameters set in step 2, translate the interception sequence until N sizes of candidate interception sequences are formed, such as Figure 4 shown.

[0098] ④Put the N intercepted sequences into the benchmark graph, substitute them into the fitness function for matching calculation, and take the minimum point of the fitness function value (MSD) as the best solution.

[0099] ⑤c1 is the acceleration factor, which is updated as Where L is the total number of iterations and l is the current number of iterations.

[0100] ⑥ Iterate the calculation cycle to update the current calculation point position, as follows:

[0101] A. Leader position update:

[0102]

[0103] Among them, F j is the position in the j-th dimension space; c2 and c3 are random numbers uniformly distributed in [0,1], which are mainly used to adjust the changing trend of the leader's position. The updated position of the leader, ub j is the upper bound of the search space, lb j is the lower bound of the search space.

[0104] B. Follower location update:

[0105]

[0106] Among them, 2≤i≤N, N is the population size, that is, the population size; Indicates the position of the follower numbered i in the j-dimensional space. Updates only with followers and Related, led indirectly by the leader.

[0107] C. Check whether it is still within the upper and lower bounds. Move the salp out of the waiting area and use formula (5) to move the salp back to the waiting area:

[0108]

[0109] in, is the updated position of the leader, ub j is the upper bound of the search space, lb j is the lower bound of the search space.i is the lower bound of the leader's updated search space; ub i is the upper bound of the leader's updated search space.

[0110] D. Substitute the calculated point after each updated position and the subsequent seven points into the fitness function for calculation. After multiple iterations, select the global optimal value, that is, the position of the minimum point of the fitness function value, until the difference between the updated position and the position before the update is less than the threshold, the iterative calculation ends, and the optimal position and fitness function value are output.

[0111] ⑥ Return to step ①, and the optimal position of the current calculation point (that is, the matching result of the point).

[0112] (4) Accuracy test. The optimal position from the first calculation point to the last calculation point is output, and the matching result is obtained. In order to verify the accuracy, the interpolated gravity anomaly and the measured gravity sequence on the reference map are compared. When the standard deviation of the difference between the two sequences is less than 2mGal, the matching result is considered excellent. If there is a GNSS receiver, the position data collected by GNSS can be used for more accurate comparison and calculation.

[0113] Example 2

[0114] like Fig.10 As shown, an embodiment of the present invention provides a group optimized underwater gravity matching navigation system, the system comprising: a data acquisition module 1, a matching module 2 and a correction module 3.

[0115] The data acquisition module 1 is used to acquire navigation data; the navigation data includes: inertial navigation position data and gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data.

[0116] The matching module 2 is used to perform data matching using the salp group algorithm according to the gravity sequence and the set reference map to obtain a matching result.

[0117] The correction module 3 is used to correct the position of the inertial navigation data according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

[0118] Example 3

[0119] An embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the group-optimized underwater gravity matching navigation method in Embodiment 1.

[0120] In one embodiment, a computer-readable storage medium is also provided, which stores a computer program. When the computer program is executed by a processor, the method of group-optimized underwater gravity matching navigation in Example 1 is implemented.

[0121] In practical application, take a certain sea area as an example:

[0122] (1) Two sea areas were selected as the research objects. The simulation method was used. The starting points of the real tracks were set to (25.945°S, 162.68°E) and (-26.2°S, 162.36°E), the heading was 45° north-west, and the number of sampling points was 20.

[0123] (2) Data preparation: Study the sea area gravity anomaly benchmark map, the output position of the inertial navigation system, the measurement value output by the gravity meter terminal, and the position information calculated by the GNSS receiver for accuracy detection.

[0124] (3) Select the fitness function. The MSD operator is selected based on the degree of consistency between the gravity anomaly sequence of the eight sampling points starting from the calculation point and the gravity anomaly profile of the reference map.

[0125] (4) The salp group algorithm is used for calculation. The first point to the last point of the measurement is selected as the calculation point sequence, and the calculation is performed cyclically.

[0126] (5) Starting from the first measured point, set the parameters of the salp swarm algorithm: ub—upper bound of the search space, lb—lower bound of the search space, N—size of the salp swarm, d—space dimension.

[0127] (6) Intercept the current calculation point and the following 7 points to form an interception sequence. Perform multiple translation operations on the interception sequence. Based on the parameters in step (5), set Initialize the salp for the position of the i-th salp (leader) in the j-dimensional space. Compare the initialized salp group sequence with the benchmark graph to obtain the best value of the fitness function and keep it.

[0128] (7) Perform iterative calculations again to update the salp swarm, including two steps: leader update and follower update. For points beyond the search space, perform position corrections. Compare the updated salp swarm sequence with the reference map to obtain the fitness function, obtain the optimal value and retain it. Repeat step (7), compare the retained optimal values ​​of multiple iterations to obtain the global optimal value; or if the optimal value cannot be determined, set the number of iterations until the difference between the best position points obtained in the two iterations is less than the specified threshold, and obtain the matching position of the current calculation point.

[0129] (8) Start calculating the next point and repeat steps (5) to (7). The matching position of each calculation point is output as the required matching sequence result.

[0130] (9) Accuracy assessment: compare and calculate the matching sequence results with the position information calculated by the GNSS receiver and evaluate the accuracy of the results.

[0131] Figures 6 to 9 The track information and the position difference between the matched track and the real track are shown. Table 1 shows the position difference statistics.

[0132] Table 1 Statistical information of the difference between the matching results and the real track

[0133]

[0134] Depend on Figures 6 to 9 As can be seen from Table 1, ① the salp swarm algorithm can be successfully used for underwater matching navigation; ② the accuracy of most points is kept within 0.5n mile, and a few points have large position differences; ③ due to the use of the segmented advancement calculation strategy of 8 sampling points, the calculation efficiency is improved, and a few points with large position differences will not affect the subsequent track path matching accuracy.

[0135] This paper proposes for the first time to use the salp swarm algorithm for underwater gravity matching navigation. Compared with other methods, the salp swarm algorithm (SSA) has the following advantages:

[0136] ①The idea is simple and easy to implement.

[0137] ②Good convergence speed.

[0138] ③Applicable to a variety of optimization problems.

[0139] ④An effective global optimization solution.

[0140] ⑤ Suitable for wide search space.

[0141] ⑥Adaptability, robustness and scalability.

[0142] ⑦Powerful neighborhood search feature.

[0143] ⑧Reduce dependence on the initial solution.

[0144] ⑨Minor parameter tuning.

[0145] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0146] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for group optimization underwater gravity matching navigation, characterized in that: The method is applied to a navigation system; The navigation system comprises: an inertial navigation system and a gravity measuring instrument terminal; the inertial navigation system and the gravity measuring instrument terminal are connected; the inertial navigation system is used to measure inertial navigation position data; the gravity measuring instrument terminal is used to collect underwater gravity navigation data; The method comprises: Acquire navigation data; the navigation data includes: the inertial navigation position data and a gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data; According to the gravity sequence and the set reference map, a salp group algorithm is used to perform data matching to obtain a matching result; The inertial navigation data is position-corrected according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

2. The method for group-optimized underwater gravity matching navigation according to claim 1, characterized in that: According to the gravity sequence and the set reference map, the salp group algorithm is used to perform data matching to obtain a matching result, which specifically includes: Determine parameter data of a salp group; the parameter data include: population size, number of iterations, spatial dimension and acceleration factor; the salp group is a group consisting of a set number of underwater gravity navigation data in a gravity sequence; the value of the population size is equal to the value of the set number; Determining the fitness function of the salp group; the fitness function is determined according to the degree of agreement between the gravity anomaly profile of the gravity measuring instrument terminal and the underwater gravity navigation data; Within the range of the spatial dimension, performing translation processing on the salp population according to the population scale to obtain a processed salp population; Performing a spatial search in the processed salp population according to the population size and the spatial dimension to obtain a cut sequence, and determining an initial leader position and an initial follower position according to the cut sequence; The fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain a matching result.

3. The method for group-optimized underwater gravity matching navigation according to claim 2, characterized in that: The fitness function is used to perform data matching according to the initial leader position, the initial follower position and the set reference graph to obtain a matching result, specifically including: Using the fitness function on the set reference graph, the initial leader position and the initial follower position are iteratively updated according to the acceleration factor to obtain an updated leader position and an updated follower position; the updated leader position and the updated follower position are both within the range of the spatial dimension; Determine a matching result based on the updated leader position and the updated follower position; The nth iteration process is: Calculating a fitness value at the nth iteration according to the fitness function, the leader position at the nth iteration, and the follower position at the nth iteration; Determine whether an iteration stop condition is met; the stop condition is: the number of n-th iterations is equal to the number of iterations or the fitness value under the n-th iteration is equal to the fitness threshold; If yes, the leader position at the nth iteration and the follower position at the nth iteration are output as the matching result; If not, the acceleration factor is updated, and the leader position at the n+1th iteration and the follower position at the n+1th iteration are determined.

4. The method for group-optimized underwater gravity matching navigation according to claim 2, characterized in that: The expression of the fitness function is: Wherein, MSD(·) is the fitness function; x is the x-axis component of the track position coordinates of the underwater gravity navigation data; y is the y-axis component of the track position coordinates of the underwater gravity navigation data; tx is the horizontal coordinate value of the track deviation; ty is the vertical coordinate value of the track deviation; Δg i (x, y) is underwater gravity navigation data; is the gravity anomaly profile; N is the population size; i is the serial number of the underwater gravity navigation data in the population.

5. The method for group-optimized underwater gravity matching navigation according to claim 3, characterized in that: The update expression of the acceleration factor is: Among them, c1 is the acceleration factor; L is the total number of iterations, and l is the current number of iterations.

6. A system for group-optimized underwater gravity matching navigation, characterized in that: The system comprises: A data acquisition module, used to acquire navigation data; the navigation data includes: inertial navigation position data and gravity sequence; the gravity sequence includes a set number of underwater gravity navigation data; A matching module, used for performing data matching using a salp group algorithm according to the gravity sequence and a set reference map to obtain a matching result; The correction module is used to perform position correction on the inertial navigation data according to the matching result to obtain corrected position data; the corrected position data is used to perform track navigation on the navigation system.

7. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the group optimized underwater gravity matching navigation method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for group-optimized underwater gravity matching navigation as described in any one of claims 1 to 5.

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