LU502256B1

LU502256B1 - Underwater topography aided navigation method based on improved salpa group algorithm

Info

Publication number: LU502256B1
Application number: LU502256A
Authority: LU
Inventors: Xiao Wang; Peng Zhang; Shuting Yuan; Jialong Sun; Wang Qiuya; Sicong Zhao; Hao Yu; Guohao Zhu; Cai Jinghui; Xia Tianyu; Huijuan Tian; Jianbo Luo; Fangzheng Ji
Original assignee: Univ Jiangsu Ocean
Priority date: 2021-10-26
Filing date: 2022-06-13
Publication date: 2023-02-09
Also published as: CN113959448B; CN113959448A

Abstract

The present disclosure discloses an underwater topography aided navigation method based on an improved salpa swarm algorithm. The underwater topography aided navigation method comprises the following steps: step 1, making a seabed digital topographic map; step 2, collecting submarine topography by multiple beams to form a sub-map; and step 3, realizing positioning of the sub-map in the seabed digital topographic map by using an improved salpa swarm algorithm. According to the method, two-dimensional matching positioning is carried out on underwater positioning by using an intelligent swarm algorithm. Compared with a traditional underwater positioning method, the method does not need to solve first-order and second-order 15 derivatives of a function, the inverse of a matrix and the like. When the objective function is complex, the calculation amount is small.

Description

UNDERWATER TOPOGRAPHY AIDED NAVIGATION METHOD BASED ON 502296

IMPROVED SALPA GROUP ALGORITHM

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of underwater positioning, in particular to an underwater topography aided navigation method based on an improved salpa swarm algorithm.

BACKGROUND

[0002] In recent years, with the exploration and utilization of the ocean in China, the requirements for positioning accuracy, diving time and concealment of underwater vehicles are getting higher and higher. AUV has its own visual and sensory system, which can move underwater by remote control or autonomous operation, thus assisting or replacing people to complete some special underwater operations. Underwater vehicles can be divided into three categories. The first category is an underwater human occupied vehicle HOV, which needs to ensure the safety of divers, so that the diving depth is limited and the working time is short. The second category is an underwater remotely operated vehicle ROV, which is unmanned and wired and needs cables to supply energy and transmit commands and data. The ROV can work for a long time, but needs a surface mother ship with a special port. The ROV has been widely used in submarine oil, natural gas, mine detection LA and submarine optical lobe laying. The third category is an underwater autonomous unmanned vehicle AUV, which is unmanned and relies on their own propulsion energy to complete the given tasks according to the pre-planned track.

The AUV has a high degree of concealment, so that it has become the key research object of many military powers in the world. A reliable navigation system is the prerequisite for the underwater vehicles to successfully complete their tasks. -

[0003] Because the inertial navigation system does not depend on any external information, does not send out signals, does not expose the existence of the vehicle itself, and is not limited by external conditions such as time, place and weather, the inertial navigation system creates conditions for an underwater vehicle to dive for a long time.

With the development of inertial navigation technology, the inertial navigation system has 502296 been continuously improved in terms of size, reliability, cost and power consumption. At present, the inertial navigation system has become the autonomous navigation and control of an underwater vehicle. However, the inertial navigation system also has its fatal defect, that is, the errors accumulate and diverge with time. In order to ensure the safe underwater navigation of the vehicle, the vehicle must emerge from the water and must be periodically calibrated and readjusted by means of other navigation technologies. This periodic position correction method not only consumes the energy of the vehicle, but also reduces the concealment. In order to overcome the above defect, using geophysical information to assist the navigation and positioning of the underwater vehicle has become a research hotspot. The available geophysical field information includes seabed topography/geomorphology information, ocean gravity field/gravity gradient field information, ocean geomagnetic field information, and so on.

[0004] With the improvement of the ability to acquire geophysical field information (gravity field, ocean topography/geomorphology, geomagnetic field, etc.) in China, the development of geophysical field measurement technology and the continuous improvement of a reference database, using a geophysical field database (or information) for aided navigation has become an important development direction in the technology field of inertial navigation of an underwater vehicle. The navigation technology based on underwater topography/geomorphology information originates from the topography matching technology that has been successfully applied on land at present. The data measured in real time by a bathymetry sensor of an underwater vehicle is matched with the pre-measured and constructed topography database information, so as to realize completely autonomous navigation and positioning and obtain high-accuracy underwater position information to assist a main inertial navigation system in correcting errors. With the development of a multi-beam bathymetric system and its gradual application to various underwater vehicles, the accuracy of submarine topography survey is getting higher and higher, and the scope is getting wider and wider. The underwater vehicle can more efficiently obtain the original database of submarine topography with higher accuracy and scan the topography distribution in real time. These advantages of the multi-beam bathymetric system create favorable conditions for submarine topography 17008856 aided matching navigation.

[0005] The submarine topography aided matching navigation system can be excellently applied to the navigation and positioning of underwater vehicles, mainly because the submarine topography has long-term invariance. Once the database is acquired, the database can be used for a long time, which is of strategic significance. This is similar to underwater geomagnetic matching navigation, gravity and gravity gradient navigation.

The principle of topography matching navigation is simple and understandable. That is, the distribution characteristics of the measured topography field are matched with the topography data of the background field. As the original topography database information contains the position information of each bathymetric point, the real-time position information can be acquired from the background field by matching the measured topography elevation distribution, which can be used to assist the main inertial navigation system in correcting errors. The key problem of the submarine topography matching navigation system lies in the matching algorithm. At present, the underwater topography matching navigation mainly uses TERCOM algorithm and ICCP algorithm, both of which are based on one-dimensional sequence matching of a single-beam bathymetric system. The real-time scanning elevation values required for matching are a series of discrete points on the moving trajectory of the underwater vehicle. The description of the original topography is not rich enough. Especially in flat topography areas or areas with more similar topography, the situation of unmatching or mismatching is prone to appear.

There is a big shortage in anti-noise ability and applicability. Therefore, with the development of the multi-beam bathymetric system and the application of this technology in the underwater vehicle, the strip-shaped two-dimensional distributed topography elevation information can be obtained, which not only enriches the description of the original topography information, but also makes up for the low efficiency of the single-beam bathymetric system. Before the traditional matching method realizes the algorithm, a lot of preparatory work needs to be done, such as solving first-order and second-order derivatives of a function, the inverse of some matrices and the like. When the objective function is complex, this work is difficult, and the calculation amount is large. Therefore, it is of great significance to study the underwater topography aided 502296 navigation method based on the improved salpa swarm algorithm.

SUMMARY

[0006] Aiming at the current shortcomings, the present disclosure provides an underwater topography aided navigation method based on an improved salpa swarm algorithm, so as to realize an efficient and accurate underwater aided navigation method.

[0007] In order to achieve the above purpose, the technical scheme used by the present disclosure is as follows:

[0008] an underwater topography aided navigation method based on an improved salpa swarm algorithm, comprising:

[0009] step 1, making a seabed digital topographic map;

[0010] step 2, collecting submarine topography by multiple beams to form a real-time topography matching map;

[0011] step 3, confirming the position and size of the seabed digital topographic map according to an inertial navigation system and an error of the inertial navigation system;

[0012] step 4, realizing positioning of a sub-map in the seabed digital topographic map by using an improved salpa swarm algorithm;

[0013] step 5, the difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning falling within a threshold range.

[0014] Preferably, in the step 1 and the step 2, the resolutions of the seabed digital topographic map and the sub-map formed by multiple beams are the same.

[0015] Preferably, the size of the sub-map is determined by the water depth of the position where a multi-beam system and an AUV are located.

[0016] Preferably, in the step 4, the underwater topography positioning of the improved salpa swarm algorithm is used, and the improved method is as follows:

[0017] S1, initializing the position of the salpa swarm by Circle chaos, wherein the formula is as follows:

05 LU502256

X, =mod(x, +0.2—{—) sin Qrex) 1)

[0018] or

[0019] S2, adding an adaptive inertia weight to the follower position, wherein the formula is as follows: œ=œ (os—acye(I-0)/T

[0020] 5 [0021] where t is the current iteration number, and T is the iteration number;

[0022] the updating formula of the new follower position is as follows:

Xt) =2 a OXj@-D+X{-D )

[0023] 2

Xv 4 . .

[0024] where “J is the position of the current generation of salpa followers 1 in the icp _ ley j-dimensional space, XCD and X Ct —D are the positions of the previous generation of salpa followers i and i-1 in the j-dimensional space, respectively;

[0025] S3, carrying out genetic algorithm local optimization on all salpas,

Xow =a*X(M+d-a)* X(2) rand <p

Xp =a HF; = X())+ X(D, rand <q

[0026]

[0027] where a is a random number between (0,1), F is the location of food source, p is the crossover probability, q is the mutation probability, XD and X) are the best salpa and the second-best salpa among selected individuals for the tournament;

[0028] S4, carrying out randomly walking for the food source,

F(t)=[0,cumsum(2r(1,)—Lcumsion(2r(1,)—L--, cumsean(2r(,)—1]

[0029]

[0030] where F(t) is the food source position, cumsum is the cumulative sum, t is the current iteration number, n is the maximum iteration number, r(t) is a random function, 0,rand <0.5 r (t)=

Lrand > 0.5

[0031]

[0032] normalizing: (Fj —a ub -Ibi)

Fi = b.—a. lb;

[0033] STE;

[0034] where F;represents the standardized position of the food source, aj and bj are the minimum and maximum values of the randomly walking step size of the j-dimensional

Ib! bt LU502256 variable, 7 and MD] are the minimum and maximum values of the t-th generation of random walk of the j-dimensional variable, respectively;

[0035] wherein the walking boundary is as follows ub, = ub, Ib, = tb,

I /

[0036]

[0037] where I increases linearly in segments with the increase of the iteration number,

I=10°%

[0038] r

[0039] t is the current iteration number, T is the maximum iteration number, and ® depends on the current generation, 0.17, w=2 057, w=3 t>4<0.75T,0=4 097, w=5

[0040] 0.957, m=5

[0041] Preferably, in the step 4, the normalized correlation algorithm is used as the positioning fitness function.

[0042] Preferably, when the normalized correlation algorithm 1s used as the positioning fitness function, the fitness function takes the inverse of the normalized correlation algorithm, and the formula of the fitness function formula is as follows: p pps RG TMS)

JS ESS

[0043]

[0044] where x is the water depth of the seabed digital topographic map, x is the average water depth of the seabed digital topographic map, y is the water depth of the real-time topography matching map, and y is the average water depth of the real-time topography matching map.

[0045] Preferably, in S3, the fitness of sea salpa individuals optimized by the genetic algorithm is compared with that of unoptimized individuals, and a greedy strategy 1s used to select sea salpa individuals with good fitness.

[0046] Preferably, the step 4 comprises the following operations:

[0047] S4.1, setting the swarm size N, the iteration number T, the problem dimension 502296 dim, the initial value ws and the final value of of a weight factor, the crossover probability p, and the mutation probability q;

[0048] S4.2, using the salpa swarm with the initializing scale N and the dimension dim, thus generating N candidate image blocks;

[0049] S4.3, sorting: sorting the swarms according to p(i), in which a half of the swarms with a high similarity value are regarded as leaders and the other half of the swarms are regarded as followers;

[0050] S4.4, updating the positions of the leaders and the followers by using the improved salpa swarm algorithm;

[0051] S4.5, calculating the fitness value of the updated swarm and updating the food source position;

[0052] S4.6, repeating S3.3-S3.5, if the set accuracy requirement or the specified maximum iteration number is reached, terminating the algorithm and outputting the position with the highest similarity;

[0053] S4.7, comparing the difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning and a threshold, wherein if the difference is less than the threshold, the matching is successful; if the difference is greater than the threshold, the second matching is performed at this location; if the second matching is still greater than the threshold, the matching at this location is abandoned, and the matching is performed at the next location.

[0054] The technical scheme can obtain the following beneficial effects.

[0055] (1) The richer original topography information is used for topography matching, which increases the matching accuracy and success rate. Before the traditional matching method realizes the algorithm, a lot of preparatory work needs to be done, such as solving first-order and second-order derivatives of a function, the inverse of some matrices and the like. When the objective function is complex, this work is difficult, and the calculation amount is large. Therefore, it is of great significance to study the underwater topography aided navigation method based on the improved salpa swarm algorithm. 17008856

[0056] (2) The positioning algorithm is transformed from one-dimension to two-dimension.

[0057] (3) Solving first-order and second-order derivatives of a function, the inverse of some matrices and the like is avoided, and the calculation amount is reduced.

[0058] (4) The salpa swarm search strategy is used to avoid the traditional search method of traversing all and reduce the calculation amount.

[0059] (5) On the basis of the basic salpa swarm algorithm, Circle mapping, genetic algorithm, adaptive inertia weight and randomly walking strategy are introduced to improve the global search ability and the ability to jump out of local optimum of the algorithm. The overall effect of the improved algorithm in eight test functions and underwater topography matching is better than the original algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

[0060] FIG. 1 isthe flow chart of underwater aided positioning.

[0061] FIG. 2 is the schematic diagram of an improved salpa positioning method.

[0062] FIG. 3 is a three-dimensional topographic map of topography 1.

[0063] FIG. 4 is a matching effect diagram of topography 1.

[0064] FIG. 5 is a three-dimensional topographic map of topography 2.

[0065] FIG. 6 is a matching effect diagram of topography 2.

[0066] FIG. 7 is a navigation effect diagram of topography.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The present disclosure will be further described in detail with reference to the following drawings and specific embodiments.

[0068] The present disclosure discloses an underwater topography aided navigation method based on an improved salpa swarm algorithm, which comprises the following steps.

[0069] S1, a seabed digital topographic map is constructed.

[0070] As shown in FIG. 1 and FIG. 2, the seabed topographic elevation data of

127.528° to 128.205° of east longitude and 27.328° to 28.005° of north latitude in the 17008856 database of the National Oceanic and Atmospheric Administration of the United States are selected as the typical test area. The 100m regular grid elevation topographic data is formed by interpolation of a 4-grid spline function, and the size of the seabed digital topographic map is 752*752.

[0071] S2, a real-time topography matching surface is acquired by multiple beams.

[0072] The 50*50 sub-map of the seabed digital topographic map is selected, and signal-to-noise ratios of 1dB, 5dB, 10dB and 15dB are added, instead of the real-time topographic matching surface acquired by multiple beams. The resolutions of the seabed digital topographic map and the sub-map formed by multiple beams are the same.

[0073] S3, the 300*300 grid is selected as the search range, and the search center is the position provided by the inertial navigation system (the basic navigation error characteristics of the inertial navigation system: 0 mean value, Gaussian white noise of 0.05 times of the distance).

[0074] S4, the improved salpa swarm algorithm is used to position the underwater topography.

[0075] S4.1, the swarm size 100, the iteration number 150, the problem dimension 2, the initial value ws and the final value of of a weight factor, the crossover probability p, and the mutation probability q are set, thus generating 50 candidate image blocks.

[0076] S4.2, sorting: the swarms according to p(i) are sorted, in which a half of the swarms with a high similarity value are regarded as leaders and the other half of the swarms are regarded as followers.

[0077] S4.3, the positions of the leaders and the followers are updated by using the improved salpa swarm algorithm.

[0078] S4.4, the fitness value of the updated swarm is calculated, and the food source position is updated.

[0079] S4.5, S4.2-S4.4 are repeated, if the set accuracy requirement or the specified maximum iteration number is reached, terminating the algorithm and outputting the position with the highest similarity. The results are shown in FIG. 4 and FIG. 6.

[0080] S4.6, positioning is carried out once every 100 grids, and the signal-to-noise ratio of 10dB is added in each real position range to simulate the real-time scanning matching 502296 map of ADCP. The difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning is compared with a threshold. If the difference is less than 10, the matching is successful. If the difference is greater than 10, the second matching is performed at this location. If the second matching is still greater than 10, the matching at this location is abandoned, and the matching is performed at the next location, as shown in FIG. 7.

[0081] S5, the difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning falls within a threshold range.

[0082] With continued reference to FIG. 2, the improved salpa algorithm is as follows.

[0083] Circle mapping is introduced to initialize the salpa individual position and increase the diversity of the initial swarm. The formula is as follows:

[0084] X =modix, +0.2-(52) sin Qrex) ‚DD

[0085] where t is the current iteration number, and T is the iteration number.

[0086] The genetic algorithm is used to optimize the updating mode of the salpa individual position, and the global search and local development abilities of the algorithm are enhanced. An adaptive inertia weight is introduced into the updating formula of the follower position, so that the global search ability and the local search ability of the algorithm are better balanced. The formula is as follows: 10087] @= 08 (as—w)e(F—N/T

[0088] where t is the current iteration number, and T is the iteration number.

[0089] The genetic algorithm local optimization is carried out on all salpas, and the optimization formula is as follows:

Xo =a* XD +U-2)*X(2), rand < p

X =a*(F, -XO+ XD), rand <q

[0090]

; LU502256

[0091] where a is a random number between (0,1), F is the location of food source, p 1s the crossover probability, q is the mutation probability, XD and X) are the best salpa and the second-best salpa among selected individuals for the tournament.

[0092] Randomly walking is carried out for the food source, and the formula is as follows:

FO =1{0, crmsum(2r(t)) — |, cumsund 2r(t,) — 1 --, cumsum(2r(t, } — 1]

[0093] È (2r() 2)

[0094] where F(t) is the food source position, cumsum is the cumulative sum, t is the current iteration number, n is the maximum iteration number, r(t) is a random function, 0 rand <0.5 r (= I

Lrand > 0.5

[0095]

[0096] Normalizing is carried out: (F; —a ub; 1b)

[0097] ca

[0098] where F;represents the standardized position of the food source, aj and bj are the minimum and maximum values of the randomly walking step size of the j-dimensional . Ib! ub! LL. . . variable, ~~! and “7 are the minimum and maximum values of the t-th generation of random walk of the j-dimensional variable, respectively.

[0099] when the normalized correlation algorithm is used as the positioning fitness function, the fitness function takes the inverse of the normalized correlation algorithm, and the formula of the fitness function formula is as follows:

PRCT BEC

N= (=x)

[00100] [00101]where x is the water depth of the seabed digital topographic map, x is the average water depth of the seabed digital topographic map, y is the water depth of the real-time topography matching map, and y is the average water depth of the real-time topography matching map.

[00102] The walking boundary is as follows ub, = "9 pp, =

I I

[00103]

[00104] where I increases linearly in segments with the increase of the iteration number, 1=10° 1

[00105] r

[00106]t is the current iteration number, T is the maximum iteration number, and ® depends on the current generation, 0.17, @=2 0.57,@=3 t><0.757,œ0=4

Jor LW =5

[00108] Randomly walking disturbs the food source, increasing the algorithm to jump out of the local optimum in the later stage. The optimization ability of the improved algorithm is evaluated through simulation experiments on eight benchmark test functions.

The results are shown in Table 1 below. The swarm is 50, the iteration number is 150, and each test function is independently performed 30 times.

Table 1: Benchmark test functions expression dimension search Theoretical interval optimal value fx) => x; iol 30 [-100, 100] 0 f,(x)= >| +] 1x:

A il 10 [-10, 10] 0 n n 2 fx => x) i=l i=l 10 {-100, 100] Ö

F(x) = max {x,}1 <i <n) 10 [-100, 100] 0 f(x) => ix," i 10 [-100, 100] 0 f(x) = 3 lx. —10cos (22x) + 10] = 10 [-5.12, 5.12] 0 f,(x)=—-20exp (-0.2 > x) — exp CE cos 27x; 0) +e+20 10 [-32, 32] 0 il f(x) = Ls (x?) - [ ] cos(x, Ai) +1 4000 “5 iz 10 [-600, 600] 0

[00109]It can be seen from Table 3 that for the five unimodal functions (F1-F5), ISSA (the improved method of the salpa swarm proposed in the present disclosure), the theoretical optimal value of O for the average value and the optimal value is obtained when solving the accuracy of F1-F4. For the multimodal functions F6, F7, F8, ISSA and CASSA (based on the crazy adaptive salpa swarm algorithm), the theoretical optimal value of 0 is obtained when solving the accuracy of F6 and F8. The standard deviation of

ISSA is always smaller than that of SCSSA (the salpa swarm algorithm of the sinusoidal cosine algorithm) and SSA (the salpa swarm algorithm). The standard deviation of ISSA on the unimodal function 1s smaller than that of CASSA on the F1-F4 function, which shows that ISSA has a good stability in the optimization and solving of low-dimensional and high-dimensional benchmark functions, and its optimization ability and the ability to jump out of local optimum are better than those of other algorithms.

Table 3: Optimization results of each algorithm function index SSA SCSSA CASSA ISSA

Optimal 3.73E-08 1.80E-08 6.66H-65 0.0000 value

F1 average 1.98E-07 &.15E-08 | 42F-64 0.00EA00 value

Standard 2,69E-07 6.05E-08 3.41E-65 0.00E+00 deviation

Optimal 3 94E-01 5.77E-06 1.14F-32 0.00E+00 value

F2 average 1,91E+00 1.03E-05 1.46H-32 0.00E+00 7502256 value

Standard 1.248400 6.06E-00 1.51#-33 0.00EA00 deviation

Optimal 5.27E+02 4.69E-09 6.62E-65 0.00E+00 value

F3 average 1. 49E+03 2.68E-05 1.08F-63 0.00E+00 value

Standard 1.15E+H03 7 16E-05 7, 83E-64 0.00E+00 deviation

Optimal 6. 14E+00 1.13E-05 1.92E-33 0.00E+00 value

F4 average 1.21E+01 6.23E-G3 3.82E-33 0.00E+00 value

Standard 3.20E+00 2. 19E-04 7.86E-34 0.00E+00 deviation

Optimal 9 40F-02 1,74E-06 7.60E-07 2.31E-06 value

F5 average 1,99H-01 5. 12E-04 9.63H-05 3.98E-05 value

Standard 8. 59E-02 4 35E-04 8428-05 3.96E-05 deviation

Optimal 2.39E+01 1.79E-10 0.00E+00 0.00E+00 value

F6 average SAZE+OI 4.73E-10 0.0GEA+00 0.00EA00 value

Standard 2.08E+01 1,90E-10 0 .00EA+GO 0.00E+00 deviation

Optimal 1.16E+60 9°76E-06 8.88F-16 8 88E-16 value LU502256

F7 average 2 64F+00 1,26E-05 8.88F-16 8.88F-16 value

Standard 8 48H-01 1.69E-06 0.GGE-+60 G.00E+00 deviation

Optimal 1.86H-04 1.52E-(9 0.0CE-+60 0.00E+00 value

FE average 1.61E-02 1.99E-02 0.00E+00 0.00E+00 value

Standard i 40E-02 5 02E-02 O.00E+00 0.00E+00 deviation

[00110] The two topographies are matched for 30 times under different signal-to-noise ratios, respectively. Refer to FIG. 3 and FIG. 4 for a three-dimensional topographic map and a matching effect diagram of topography 1. Refer to FIG. 5 and 6 for a three-dimensional topographic map and a matching effect diagram of topography 2. As shown in Table 4, Table 5, Table 6 and Table 7, it can be seen that the success rate of

ISSA in underwater topography positioning is much higher than that of the other two algorithms (if the errors in the X and Y directions are less than 5 grids, the matching is considered to be successful), and the circular probability error of ISSA is also smaller than that of the other two algorithms, but it is still relatively large for underwater positioning. The reason is that during the positioning process, the algorithm falls into the local optimum, resulting in a great difference between several positioning results and the real position.

[00111]It can be seen from table 7 that the error between the corrected track and the real track is always within 5 grids in the underwater aided navigation using S4.6 method.

Table 4: Matching success rate of topography 1

Signal-to-noise search success times failure times success rate ratio strategy

SSA Ii 19 36 i CASSA 12 18 40 17008856

ISSA 12 18 40

SSA 19 i 63

CASSA 23 7 77

ISSA 26 4 87

SSA 25 5 83

CASSA 20 10 67

ISSA 29 1 97

SSA 20 10 67

CASSA 19 11 63

ISSA 29 1 97

Table 5: Matching success rate of topography 2

Signal-to-noise search success times failure times success rate ratio strategy

SSA 25 5 83 1 CASSA 27 3 90

ISSA 28 2 93

SSA 27 3 90 5 CASSA 28 2 93

ISSA 30 Ö 100

SSA 26 4 87 10 CASSA 29 1 97

ISSA 30 Ö 100

SSA 27 3 90

Table 5: Continued

Signal-to-noise search success times failure times success rate ratio strategy 15 CASSA 30 Ö 100

ISSA 30 Ö 100

Table 6: Circular probability error 502296

Signal-to-noise search CEP of topography 1 CEP of topography 2 ratio strategy

SSA 8963214 32.70798 1 CASSA 87. 15858 23 61276

ISSA 79 6752 18 96885

SSA 62.55282 23.40401

CASSA 51.993 1977558

ISSA 33.08037 1.411544

SSA 52 63985 26 66022

CASSA 63.99632 14.0209

ISSA 23.33995 (0.98218

SSA 61.24059 23.36195

CASSA 62 89886 0.451328

ISSA 23 46617 0.451328

Table 7: Underwater navigation errors

Positioning i 2 3 4 5 6 7 point

Error in x 0 4 ~1 I 4 1 1 direction

Error in y 2 -4 0 -3 -1 1 3 direction

Claims

Claims LU502256

1. An underwater topography aided navigation method based on an improved salpa swarm algorithm, comprising: step 1, making a seabed digital topographic map; step 2, collecting submarine topography by multiple beams to form a real-time topography matching map; step 3, confirming the position and size of the seabed digital topographic map according to an inertial navigation system and an error of the inertial navigation system; step 4, realizing positioning of a sub-map in the seabed digital topographic map by using an improved salpa swarm algorithm; step 5, the difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning falling within a threshold range.

2. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 1, wherein in the step 1 and the step 2, the resolutions of the seabed digital topographic map and the sub-map formed by multiple beams are the same.

3. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 2, wherein the size of the sub-map is determined by the water depth of the position where a multi-beam system and an AUV are located.

4. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 1, wherein in the step 4, the underwater topography positioning of the improved salpa swarm algorithm is used, and the improved method is as follows: S1, initializing the position of the salpa swarm by Circle chaos, wherein the formula is as follows: Xi = mod(x, +02- (52) sin Qrex) D

S2, adding an adaptive inertia weight to the follower position, wherein the formula is 502296 as follows: = 05 (os—wcye(T —1)/T where t is the current iteration number, and T is the iteration number; the updating formula of the new follower position is as follows: Xi) = La QU -D+X"C-D ) where Xi is the position of the current generation of salpa followers i in the j-dimensional space, Xie -1 and Xi -1 are the positions of the previous generation of salpa followers 1 and 1-1 in the j-dimensional space, respectively; S3, carrying out genetic algorithm local optimization on all salpas, Xe —a*X(H+1-a)*X(2), rand< p Xo, =a*(F, -XW)+XO), rand <q where a is a random number between (0,1), F is the location of food source, p is the crossover probability, q is the mutation probability, XM and X) are the best salpa and the second-best salpa among selected individuals for the tournament; S4, carrying out randomly walking for the food source, FO ={0, cumsum(2r(t,) — 1, cumsum(2r(t,) — 1, cumsum(2r (1 } — 1} where F(t) is the food source position, cumsum is the cumulative sum, t is the current iteration number, n is the maximum iteration number, 1(t) is a random function, r (De A rand <0.5 Lrand > 0.5 normalizing: p-C a Ty CE; where F; represents the standardized position of the food source, aj and b; are the minimum and maximum values of the randomly walking step size of the j-dimensional variable, Ib; and ub are the minimum and maximum values of the t-th generation of random walk of the j-dimensional variable, respectively; wherein the walking boundary is as follows ub, = "De gp 10 I 0! where I increases linearly in segments with the increase of the iteration number, 1=10° L T t is the current iteration number, T is the maximum iteration number, and © depends on the current generation, 017,œ=2 057,0 =3 t><0.757,œ=4 097, 0=>5 09537. w=0

5. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 1, wherein in the step 4, the normalized correlation algorithm is used as the positioning fitness function.

6. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 5, wherein when the normalized correlation algorithm is used as the positioning fitness function, the fitness function takes the inverse of the normalized correlation algorithm, and the formula of the fitness function formula is as follows: p (rm ELIOT N= Y=») where x is the water depth of the seabed digital topographic map, x is the average water depth of the seabed digital topographic map, y is the water depth of the real-time topography matching map, and y is the average water depth of the real-time topography matching map.

7. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 4, wherein in S3, the fitness of sea salpa individuals 502296 optimized by the genetic algorithm is compared with that of unoptimized individuals, and a greedy strategy is used to select sea salpa individuals with good fitness.

8. The underwater topography aided navigation method based on the improved salpa swarm algorithm according to claim 1, wherein the step 4 comprises the following operations:

S4.1, setting the swarm size N, the iteration number T, the problem dimension dim, the initial value ws and the final value of of a weight factor, the crossover probability p, and the mutation probability q;

S4.2, using the salpa swarm with the initializing scale N and the dimension dim, thus generating N candidate image blocks; S43, sorting: sorting the swarms according to p(i), in which a half of the swarms with a high similarity value are regarded as leaders and the other half of the swarms are regarded as followers;

S4.4, updating the positions of the leaders and the followers by using the improved salpa swarm algorithm;

S4.5, calculating the fitness value of the updated swarm and updating the food source position;

S4.6, repeating S3.3-S3.5, if the set accuracy requirement or the specified maximum iteration number is reached, terminating the algorithm and outputting the position with the highest similarity;

S4.7, comparing the difference between the horizontal position change between the kth position result and the (k-1)th positioning result and the horizontal position change between the basic navigations during the kth positioning and the (k-1)th positioning and a threshold, wherein if the difference is less than the threshold, the matching is successful; if the difference is greater than the threshold, the second matching is performed at this location; if the second matching is still greater than the threshold, the matching at this location is abandoned, and the matching is performed at the next location.