CN-122020925-A - Long baseline topology design system, device and storage medium for underwater sound positioning based on fusion evolution algorithm

CN122020925ACN 122020925 ACN122020925 ACN 122020925ACN-122020925-A

Abstract

The invention belongs to the field of underwater sound, and particularly relates to a long baseline topology design system, equipment and a storage medium for underwater sound positioning based on a fusion evolution algorithm. The invention aims to solve the problem that the positioning accuracy is low in a long baseline array type designed by a long baseline topology design algorithm under the existing complex terrain. The design system comprises the steps of firstly obtaining a direct sound range of candidate beacon points through simulation, secondly forming a coverage mask matrix, thirdly performing global search screening on the candidate beacon points based on a genetic algorithm, selecting population individuals capable of meeting the requirements of an outer layer objective function, namely effective coverage rate, forming preliminary population individuals, fourthly transferring the population individuals formed by the genetic algorithm to particle swarms, updating and iterating the original population individuals through updating speed and positions of the particle swarms, fifthly performing an internal multi-constraint mechanism on the population individuals selected by the particle swarms, and outputting final population individuals, namely matrix optimization results when the internal GDOP and navigation error requirements are met.

Inventors

LI HAIPENG
ZHENG CUIE
SUN DAJUN
ZHANG JUCHENG
HAN YUNFENG
HONG XIAOPING

Assignees

青岛哈尔滨工程大学创新发展中心

Dates

Publication Date: 20260512
Application Date: 20260123

Claims (10)

1. The long baseline topology design system for underwater sound positioning based on the fusion evolution algorithm is characterized by comprising the following components: The system comprises an environment initialization module, a parameter initialization module, a candidate beacon point position generation module, a coverage mask generation module and a long baseline topology design optimization module; The environment initialization module is used for initializing environment data; the parameter initialization module is used for initializing algorithm parameters in the long baseline topology design optimization module; the candidate beacon point position generation module is used for generating according to the input route and the offset distance Initial candidate beacon points; is a positive integer; The coverage mask generation module is used for generating a coverage mask according to the following conditions Generating an overlay mask matrix by the initial candidate beacon points; the long baseline topology design module is used for according to The initial candidate beacon points and the coverage mask matrix generate a long baseline topology.
2. The system for long baseline topology design for underwater sound localization based on the fusion evolution algorithm of claim 1, wherein the environmental data to be initialized in the environmental initialization module comprises submarine topography elevation data and sound velocity profile data.
3. The system for designing the long-baseline topology for underwater sound localization based on the fusion evolution algorithm according to claim 2, wherein the parameter initialization module is used for initializing algorithm parameters in the long-baseline topology design optimization module, and comprises population scale S and total iteration times Number of iterations of genetic algorithm Number of PSO stage iterations Initial crossover probability for genetic algorithm Crossover probability variation range of genetic algorithm Initial variation probability of genetic algorithm Variation probability variation range of genetic algorithm PSO first learning factor PSO second learning factor Initial inertial weight of PSO Coverage of target Allowable maximum GDOP value Inertial navigation drift rate and navigation error threshold value Inertia weight decay factor 。
4. The long baseline topology design system for underwater sound localization based on a fusion evolutionary algorithm of claim 3, wherein said candidate beacon point location generation module generates from an input course and offset distance The specific process is as follows: a1, calculating a route buffer area according to an input route and an offset distance; The route includes a number a of waypoints, A2, gridding the route buffer area to obtain a gridded route buffer area; A3, setting a threshold value, and screening grid points in the gridded route buffer area according to the threshold value to obtain And initial candidate beacon points.
5. The long baseline topology design system for underwater sound localization based on a fusion evolution algorithm of claim 4, wherein said coverage mask generation module is configured to generate a coverage mask based on Generating an overlay mask matrix by the initial candidate new punctuation; The specific process is as follows: B1 calculation of The method comprises the following specific processes of: Calculating the maximum direct sound range of each candidate beacon point at different azimuth angles by using Bellhop3D sound line model as Direct sound ranges of the initial candidate beacons; B2 according to The direct sound ranges of the candidate beacons are calculated to obtain coverage conditions from each grid point to each beacon, and a coverage mask matrix M is generated according to the coverage conditions from each grid point to each beacon; Wherein, the Representing the elements covering the j-th row k column of the mask matrix M, When the value of 1 indicates that the jth mesh point is covered by the kth beacon; when the value of 0 indicates that the jth mesh point is covered by the kth beacon, j= {1,2,., },k={1,2,...,N}。
6. The long baseline topology design system for underwater sound localization based on the fusion evolution algorithm of claim 5, wherein the long baseline topology design module is based on the following The initial candidate beacon points and the coverage mask matrix generate a long baseline topological structure, and the specific process is as follows: S1 according to Generating an initial population of a genetic algorithm by the initial candidate new punctuation; constructing an individual fitness function of a genetic algorithm according to the coverage mask matrix; Global searching is carried out by using a genetic algorithm to obtain an optimized population of the genetic algorithm; s2, taking the optimized population of the genetic algorithm as an initial particle swarm of the particle swarm algorithm, Constructing a particle fitness function of a particle swarm algorithm according to the coverage mask matrix; Carrying out local search by using a particle swarm algorithm to obtain an optimal population of the particle swarm algorithm; And S3, obtaining a long baseline topological structure according to the optimal population of the particle swarm algorithm.
7. The long baseline topology design system for underwater sound localization based on the fusion evolution algorithm of claim 6, wherein the step of S1 is based on Generating an initial population of a genetic algorithm by the initial candidate new punctuation; constructing an individual fitness function of a genetic algorithm according to the coverage mask matrix; the genetic algorithm is used for global searching to obtain an optimized population of the genetic algorithm, and the specific process is as follows: s1.1 according to Generating an initial population of a genetic algorithm by the initial candidate new punctuation; the specific process is that an initial population is randomly generated in a candidate point set, and the initial population of the genetic algorithm comprises the following steps: A long baseline topology represented by N individuals; Constructing an individual fitness function of a genetic algorithm according to the coverage mask matrix, and expressing the individual fitness function as follows: Wherein X represents an individual, The indication function is represented by a representation of the indication function, Representing the target coverage; S1.2, calculating the fitness value of each individual in the population, S1.3, sequentially performing selection, crossing and mutation operations according to the fitness value of each individual in the current generation population to obtain a new generation population; S1.4, repeating the step S1.3, and stopping iteration when the stopping condition is met, so as to obtain the optimized population of the genetic algorithm.
8. The system for designing a long baseline topology for underwater sound localization based on the fusion evolution algorithm according to claim 7, wherein the optimized population of the genetic algorithm is used as the initial particle swarm of the particle swarm algorithm in the step S2, Constructing a particle fitness function of a particle swarm algorithm according to the coverage mask matrix; The method comprises the steps of using a particle swarm algorithm to perform local search to obtain an optimal population of the particle swarm algorithm, wherein the specific process is as follows: s2.1, taking an optimized population of a genetic algorithm as an initial particle swarm of a particle swarm algorithm; Constructing a particle fitness function of a particle swarm algorithm according to the coverage mask matrix, and expressing the particle fitness function as follows: Wherein Y represents a particle, The indication function is represented by a representation of the indication function, Representing the target coverage; s2.2, calculating the speed of each particle of the t+1st generation particle group according to the speed and the position of each particle of the t generation particle group, the individual historical optimal position of each particle of the t generation particle group and the global optimal position of the t generation particle group; The ith particle velocity of the (t+1) th generation particle group is calculated and expressed as: In the formula, Represents the t generation inertia weight, Represents the velocity of the ith particle of the t th generation, A first random vector representing the t th generation, A second random vector representing the t th generation, Represents the individual historical optimal position of the ith particle of the t-th generation particle group, Represents the global optimal position of the t-th generation particle swarm, Representing the position of the ith particle of the t-th generation particle group; the representation of the t-th generation inertial weight The calculation formula is expressed as: In the formula, The number of iterations of the PSO stage is indicated, Representing the initial inertial weight of the PSO, Representing inertial weight decay coefficients; s2.3, updating according to the position of each particle of the t-th generation particle swarm and the speed of each particle of the t+1th generation particle swarm to obtain the position of each particle of the t+1th generation particle swarm; the position of the ith particle of the (t+1) th generation particle group is updated, and the position is expressed as: S2.4, calculating the fitness value, the GDOP value and the navigation error value of each particle of the t+1st generation particle swarm; according to the fitness value of each particle of the t+1st generation particle swarm, obtaining the individual historical optimal position of each particle of the t+1st generation particle swarm and the global optimal position of the t+1st generation particle swarm; s2.5, repeating the steps S2.2 to 2.4, stopping iteration when the stopping condition of the particle swarm algorithm is met, and taking N particles with the highest adaptability in the particle swarm at the moment as the optimal population of the particle swarm algorithm; the stopping condition of the particle swarm algorithm is that a first constraint condition and a second constraint condition are simultaneously met; the first constraint is formulated as: In the formula, Represents the average value of the GDOP for the N particles with the highest fitness, The navigation error values of the N particles having the highest fitness among the particle groups are shown.
9. A computer storage medium having stored therein at least one instruction loaded and executed by a processor to implement the fusion evolution algorithm-based long baseline topology design system for underwater sound localization of any one of claims 1 to 8.
10. A long baseline topology design device for underwater sound localization based on a fusion evolution algorithm, characterized in that the device comprises a processor and a memory, wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the long baseline topology design system for underwater sound localization based on the fusion evolution algorithm as claimed in any one of claims 1 to 8.

Description

Long baseline topology design system, device and storage medium for underwater sound positioning based on fusion evolution algorithm Technical Field The invention belongs to the field of underwater sound, and particularly relates to a long baseline topology design system, equipment and a storage medium for underwater sound positioning based on a fusion evolution algorithm. Background Long Baseline (LBL) positioning is a key technology for realizing high-precision absolute positioning of an underwater platform. For long baseline systems, the configuration of the subsea reference array directly determines the accuracy of the positioning of the system as a whole. The conventional LBL array design usually adopts a regular arrangement mode, such as a triangle or square array, but with the increase of the complexity of deep sea operation, the conventional arrangement array is difficult to meet the positioning precision requirement in the scene with larger topography fluctuation. The existing method comprises the steps of optimizing an array structure on the premise of keeping geometric symmetry, simplifying an acoustic resolving process, facilitating array layout control, based on a precision factor minimization method, based on the principle of geometric precision factor (GDOP) minimization, increasing the number of beacons, improving positioning performance by number replacement precision, and based on a traditional evolutionary algorithm, realizing array optimization design on flat terrain. The prior art has the following defects: Geometric symmetry and GDOP-based minimization are adopted to neglect the complexity of the AUV actual operation track and the submarine topography, and the array type can only ensure the positioning precision of the central position and has obvious precision disadvantages in a specific direction; The operation cost and time consumption are greatly increased in the actual engineering by only increasing the number of beacons to improve the positioning precision. Based on the traditional evolutionary algorithm, the matrix optimization design is realized on flat terrains, only GDOP is usually used as a single optimization target, direct constraint on the number of beacons is lacked, positioning accuracy cannot be maintained under the constraint condition of complex terrains, and the acoustic propagation paths between partial beacons and underwater platforms can be completely shielded by complex terrains (such as ridge and sea mountain), so that the beacons calculated by the traditional optimization model cannot work normally, actual positioning coverage rate is greatly reduced, and large positioning errors are introduced. Disclosure of Invention The invention aims to solve the problem that the positioning accuracy is low in a long baseline array type designed by a long baseline topology design algorithm under the existing complex terrain. A long baseline topology design system for underwater acoustic localization based on a fusion evolution algorithm is provided, comprising: The system comprises an environment initialization module, a parameter initialization module, a candidate beacon point position generation module, a coverage mask generation module and a long baseline topology design optimization module; The environment initialization module is used for initializing environment data and is responsible for preparing and standardizing the environment data for subsequent calculation (such as acoustic propagation modeling, ocean simulation and the like). The core task is to convert the raw data into a structured, computable form; the parameter initialization module is used for initializing algorithm parameters in the long baseline topology design optimization module; the candidate beacon point position generation module is used for generating according to the input route and the offset distance Initial candidate beacon points; is a positive integer; The coverage mask generation module is used for generating a coverage mask according to the following conditions Generating an overlay mask matrix by the initial candidate beacon points; the long baseline topology design module is used for according to The initial candidate beacon points and the coverage mask matrix generate a long baseline topology. The long baseline topology structure comprises N candidate beacon positions, wherein N is a positive integer; A computer storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the fusion evolution algorithm-based long baseline topology design system for underwater sound localization. The long baseline topology design device for underwater sound localization based on the fusion evolution algorithm is characterized by comprising a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to realize the long baseline topology design system f