CN117010132B - Space array position optimization method and system of underwater multi-base sound system - Google Patents
Space array position optimization method and system of underwater multi-base sound system Download PDFInfo
- Publication number
- CN117010132B CN117010132B CN202311254865.6A CN202311254865A CN117010132B CN 117010132 B CN117010132 B CN 117010132B CN 202311254865 A CN202311254865 A CN 202311254865A CN 117010132 B CN117010132 B CN 117010132B
- Authority
- CN
- China
- Prior art keywords
- underwater multi
- base
- base sound
- underwater
- optimization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 230000004927 fusion Effects 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims description 19
- 230000035772 mutation Effects 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 230000005855 radiation Effects 0.000 claims description 7
- 230000004308 accommodation Effects 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 abstract description 13
- 238000012821 model calculation Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 23
- 230000002068 genetic effect Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007620 mathematical function Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Radar, Positioning & Navigation (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Remote Sensing (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The invention provides a space array position optimization method and a space array position optimization system of an underwater multi-base sound receiving system, wherein the space array position optimization method comprises the following steps: s1, establishing constraint conditions according to characteristics of a sonar system, and determining coverage of the underwater multi-base sonar system; s2, establishing a position coordinate model according to the constraint conditions and the coverage range of the underwater multi-base sound receiving system; s3, setting initial base sound receiving position coordinates according to the constraint conditions, performing iterative optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound receiving system. The method and the device can gradually reduce errors and improve the positioning accuracy of the underwater multi-base sound receiving system through continuous iterative optimization of the position coordinate model; and by combining the Monte Carlo method and the K/N fusion criterion, the searching capability is further enhanced, the expansibility of the position coordinate model calculation is improved, and the performance and the accuracy of the underwater multi-base sound receiving system are further improved.
Description
Technical Field
The invention relates to the technical field of underwater communication, in particular to a space array position optimization method and system of an underwater multi-base sound receiving system.
Background
Along with the continuous improvement of ocean information technology and application requirements, the multi-base sonar system is widely applied due to the performance advantages of active and passive sonar. The space array position structure of the multi-base sonar is a key factor affecting the overall performance of the system, and on the basis of not increasing a base platform, how to optimally design the array structure of the multi-base sonar system is important in facing different array scenes and requirements.
The underwater sound target detection positioning technology is one of key technologies in the marine application fields such as environmental reconnaissance, monitoring and tracking, resource mining, deep sea exploration and the like. In recent years, with the great development of ocean information technology, the target detection and positioning technology is facing new serious tests. If the target radiation noise and the echo intensity are greatly reduced, the noise of the marine environment increases year by year. This results in the need for a single-platform, uncooperative probe positioning technique that transitions to a multi-platform collaboration technique with multidimensional interworking, spatial sharing, and information resource complementation. The multi-base sonar system is widely applied as an important multi-platform cooperation mode due to the fact that the multi-base sonar system has a longer detection distance and stronger positioning, countering and anti-diving reconnaissance capability.
A cooperative system, typically consisting of one transmitting station (or multiple) and multiple, separately deployed receiving stations, is referred to as a multi-base system. In the multi-base sonar system, each site is configured by different sonar nodes. In general, a transmitting station is equipped with an active sonar for co-location, and has the capability of receiving target echo signals. Whereas the scattered receiving sites are equipped with passive sonar, only target echoes can be received. The configuration strategy of the node division can simultaneously have the performance advantages of active/passive sonar, ensure self-secrecy and further improve the detection potential capability, so that the configuration strategy has wide application prospect.
Chinese patent CN113820715B discloses a target positioning method using array element level multi-base data fusion, which estimates a region range according to the total number of bases and the array element spacing, performs array element level data fusion, constructs multi-base steering vectors, and uses a spatial spectrum estimation algorithm to complete target positioning.
However, the above technical solution cannot optimize the deployment position of the underwater multi-base sonar system, cannot guarantee the maximization of the underwater sonar coverage, and is easy to overlap the sonar coverage, resulting in sonar resource waste.
Therefore, a space optimization method capable of guaranteeing the maximum coverage of the underwater multi-base sonar system and improving the overall positioning accuracy of the sonar system is sought, and the technical problem to be solved by the person skilled in the art is needed.
Disclosure of Invention
In view of the above, the invention provides a space array position optimization method of an underwater multi-base sonar system, which can gradually reduce errors, improve the positioning accuracy of the underwater multi-base sonar system and improve the performance of the underwater multi-base sonar system by continuous iterative optimization of a position coordinate model.
The technical scheme of the invention is realized as follows:
in a first aspect, the present invention provides a method for optimizing a spatial array position of an underwater multi-base sound receiving system, comprising the steps of:
s1, establishing constraint conditions according to characteristics of a sonar system, and determining coverage of the underwater multi-base sonar system;
s2, establishing a position coordinate model according to the constraint conditions and the coverage range of the underwater multi-base sound receiving system;
s3, setting initial base sound receiving position coordinates according to the constraint conditions, performing iterative optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound receiving system.
On the basis of the above technical solution, preferably, step S2 specifically includes:
determining a measurement mode of the underwater multi-base sound system according to the coverage range of the underwater multi-base sound system;
establishing an optimization function by combining the coverage range, the measurement mode and the constraint condition of the underwater multi-base sound receiving system;
and adjusting the optimization function, and establishing a position coordinate model according to the adjusted optimization function.
On the basis of the above technical solution, preferably, the formula of the optimization function is as follows:
；
wherein u is 0 And (3) representing a fusion detection result of the underwater multi-base sonar system, wherein K represents the number of times the sonar system detects a target, N represents the number of times of judging the result, and f (u) represents the result of the optimization function.
On the basis of the above technical solution, preferably, step S3 specifically includes:
s31, setting W initial base sound position coordinates according to constraint conditions, wherein the initial base sound position coordinates are used as individuals of a first population;
s32, after the parent individuals in the first population are selected pairwise repeatedly, performing cross operation to generate 2W new individuals;
s33, the 2W new individuals form a coverage area of an underwater multi-base sonar system, and an adaptability value of the base sonar is determined according to the coverage area of the underwater multi-base sonar system;
s34, arranging the fitness values in descending order according to the order from large to small, and selecting the first W individuals in the fitness values to form a second population;
s35, performing mutation operation on the second population to obtain a third population;
s36, repeating the steps S32-S35 for M times to perform iterative optimization, and stopping iterative optimization when M meets the termination condition, and outputting the actual position coordinates of the underwater multi-base sound receiving system.
On the basis of the above technical solution, preferably, the cross formula adopted by the cross operation in step S32 is specifically as follows:
；
wherein z is [0,1]Random real number, X is new individual generated by crossing, X 1 Represents the first parent, X 2 Representing a second parent individual.
Still further preferably, the spatial array position optimizing method further includes the steps of:
and S4, optimizing the array position of the underwater multi-base sound system according to the actual position coordinates of the underwater multi-base sound system to obtain an optimal array position node of the underwater multi-base sound system.
On the basis of the above technical solution, preferably, step S4 specifically includes:
the actual position of the underwater multi-base sound receiving system forms a target radiation area, and the actual position coordinates are subjected to cross operation to generate a fourth population;
calculating the fitness value of the individuals in the fourth population, and generating an objective function according to the fitness value;
calculating error geometric distribution of each vertex in the target radiation area, and arranging the error geometric distribution of each vertex in a descending order;
performing variation operation on the error geometric distribution of each vertex to obtain variation probability;
calculating a weight coefficient of variation probability according to the fitness value of the individuals in the fourth population;
and optimizing the array position of the underwater multi-base sound receiving system according to the weight coefficient to obtain an optimal array position node of the underwater multi-base sound receiving system.
On the basis of the above technical solution, preferably, the mutation probability P m The formula of (2) is:
；i=1，2，…，W；
wherein k is 1 And k 2 Represents a random real number on (0, 1), W represents the number of the population, f i Indicating the fitness value of the ith individual in the population, f max Representing the maximum fitness value of the individuals in the population,mean value of fitness values representing all individuals in the population, +.>A weight coefficient representing a variation probability, wherein the weight coefficient of the variation probability is a normalized coefficient on (0, 1).
On the basis of the technical proposal, preferably, the weight coefficient of the variation probabilityThe method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the j=1, 2, …, W and i+.j;
wherein f j And the fitness value of the jth population is represented.
In a second aspect, the present invention provides a space array position optimizing system of an underwater multi-base sound receiving system, and the space array position optimizing method of the underwater multi-base sound receiving system according to any one of the above-mentioned aspects is adopted, including:
the constraint module is used for establishing constraint conditions according to the characteristics of the underwater multi-base sonar system and determining the coverage range of the underwater multi-base sonar;
the first optimization module is used for establishing an optimization function according to the constraint conditions and the coverage range of the underwater multi-base sonar, and adjusting the optimization function to establish a position coordinate model;
and the iteration module is used for setting initial base sound accommodation position coordinates according to the constraint conditions, carrying out iteration optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound accommodation system.
Compared with the prior art, the space array position optimization method of the underwater multi-base sound receiving system has the following beneficial effects:
(1) By establishing effective constraint conditions according to the characteristics of the sonar, establishing an optimization function according to the coverage range and the constraint conditions of the underwater multi-base sonar, and adjusting the optimization function to generate a position coordinate model, the position coordinate model meets the actual application requirements, and by continuous iterative optimization of the position coordinate model, the error can be gradually reduced, and the positioning accuracy of the underwater multi-base sonar system is improved;
(2) Calculating the optimal position coordinates of the underwater multi-base sound receiving system through the fixed variation probability in the genetic algorithm, avoiding the problem of local optimal solution, further enhancing the searching capability by combining the Monte Carlo method and the K/N fusion criterion, improving the expansibility of the position coordinate model calculation, and further improving the performance and the precision of the underwater multi-base sound receiving system;
(3) The coverage area of the underwater multi-base sonar system is processed by adopting a grid discretization processing mode, the weighted average of error geometric distribution numerical values of the underwater multi-base sonar in the coverage area is solved, the calculated amount of a coordinate model is reduced, and therefore the optimal position distribution of the underwater multi-base is accelerated to be solved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing the spatial array position of an underwater multi-base sound receiving system of the present invention;
FIG. 2 is a flow chart of an adaptive genetic algorithm of the spatial array position optimization method of the underwater multi-base sound absorption system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1, the invention provides a space array position optimization method of an underwater multi-base sound receiving system, which comprises the following steps:
s1, establishing constraint conditions according to the characteristics of a sonar system, and determining the coverage range of the underwater multi-base sonar system. The constraint conditions comprise the number of the bases, the minimum distance limitation among the bases, the positioning precision of the multi-base sound receiving system and the like.
It will be appreciated that sonar systems transmit pulses of sound waves, which are reflected when the pulses encounter a target object. By measuring the propagation time of the pulse and the received echo intensity, the distance and position of the target object from the sonar system can be calculated. Sonar may be used underwater and in air to provide information about objects.
Based on the characteristics of the sonar system, constraint conditions can be established, such as determining coverage areas formed among the underwater multi-base sonar systems according to the number of the underwater bases, the minimum distance among the bases, the positioning accuracy of the multi-base sonar systems and the like, and maximizing the coverage of the sonar system, so that the monitoring and detecting capability of the sonar system is improved.
In an embodiment of the present application, the method for calculating the effective coverage of the multi-base sonar system specifically includes: and defining the area range determined by the fusion detection probability of the multi-base sonar system greater than 0.7 as an effective coverage range. And calculating the effective coverage of the multi-base sonar by adopting a Monte Carlo method. The Monte Carlo method calculates the curved surface area of the multi-base, and utilizes the Monte Carlo randomization idea to convert the area solving problem into a probability problem that the random event falls into a certain area. Namely, uniformly and randomly extracting a certain number of points G in the target search range, counting the number of points which are all located in the effective coverage area, and marking the number as G, wherein the size of the effective coverage area can be approximately equal to (G/G) x S, and S is the area of the target search area. In addition, the larger the extraction point G is, the smaller the area difference value calculated in two modes is.
Specifically, the method for calculating the fusion detection probability comprises the following steps: firstly, determining false alarm probability P of each sonar node f Namely, under the condition of only background noise, the probability that the signal of the receiving end exceeds the threshold voltage is calculated, and then the fusion detection probability of the multi-sonar base system is calculated according to the fusion rule, namely, the probability that the signal of the receiving end exceeds the threshold voltage under the signal noise background, wherein the false alarm probability P of each sonar node f The calculation formula of (2) is as follows:
multi-base sound system fusion detection probability P d The calculation formula of (2) is as follows:
wherein r represents the signal of the receiving end, D is the amplitude of the transmitting signal, V T Represents a threshold voltage, I 0 For a modified zero-order bessel function,representing the variance of gaussian white noise.
And determining the region range by fusing detection probabilities through a multi-base sonar system, and calculating the effective coverage range by using a Monte Carlo method.
S2, establishing a position coordinate model according to the constraint conditions and the coverage range of the underwater multi-base sound receiving system.
According to the method and the device, the optimal position of the underwater base is determined by establishing the position coordinate model so as to cover the required area to the greatest extent, meanwhile, the position coordinate model can optimize the arrangement of the underwater base according to the propagation characteristics of sonar so as to achieve the optimal coverage, the performance and the efficiency of an underwater sonar system are improved, the number of sonar equipment is reduced, fewer energy sources are used, and therefore maintenance and operation costs are reduced.
Specifically, step S2 specifically includes:
determining a measurement mode of the underwater multi-base sound system according to the coverage range of the underwater multi-base sound system;
establishing an optimization function by combining the coverage range, the measurement mode and the constraint condition of the underwater multi-base sound receiving system;
and adjusting the optimization function, and establishing a position coordinate model according to the adjusted optimization function.
It will be appreciated that the measurement of the underwater multi-base sonar system includes the area of the coverage area, the effective detection distance, and the dead zone minimization, where the sonar system coverage area may be an area on a two-dimensional plane or a volume in three-dimensional space. According to the attenuation degree of sonar signals propagating under water or the measurement of signal intensity, the maximum distance that a sonar system can effectively detect a target can be determined; since the sonar system has an area which cannot be covered, namely a blind area, the performance of the sonar system is improved by minimizing the blind area.
In the embodiment of the application, the detection probability of the sonar system can be determined by fusing the detection results of a plurality of single nodes, so that the coverage areas of a plurality of base sonar systems are fused by adopting the fusion criterion, the performances of different base sonar are fully utilized, and the maximum coverage area of the underwater multi-base sonar system is further determined.
In a preferred embodiment of the present application, the optimization function is established using K/N fusion criteria.
Further, the formula of the optimization function is as follows:
wherein u is 0 And (3) representing a fusion detection result of the underwater multi-base sonar system, wherein K represents the number of times the sonar system detects a target, N represents the number of times of judging the result, and f (u) represents the result of the optimization function.
As will be appreciated by those skilled in the art, the above-described optimization function indicates that an underwater multi-base sound system is considered to detect a target when there are no less than K results for the underwater multi-base sound system to detect a target.
In a further embodiment of the present application, the present application uses the monte carlo method instead of the integration method to calculate the effective coverage of the underwater multi-base sound system.
Specifically, collecting detection data of underwater sound receiving systems of all bases;
generating a sample set by using a Monte Carlo method, and calculating a sonar system observation value corresponding to each sample data;
and for the observed values of the sonar systems of the bases, carrying out fusion processing and analysis on the observed values of the sonar systems by using a K/N fusion rule to obtain a fusion detection result of the underwater multi-base sonar system.
In the embodiment, the effective coverage area of the underwater multi-base sound receiving system is calculated through the Monte Carlo method and the K/N fusion rule, so that the calculation accuracy can be improved, and meanwhile, the robustness and the anti-interference capability of the underwater multi-base sound receiving system can be enhanced.
S3, setting initial base sound receiving position coordinates according to the constraint conditions, performing iterative optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound receiving system.
In the embodiment of the application, various constraint relations, such as effective detection range of sonar, avoidance of dead zones, minimization of cost and the like, are considered in the iterative optimization process, and the position coordinate model can be better adjusted to meet the actual application requirements; through continuous iterative optimization of the position coordinate model, errors can be gradually reduced, and the positioning accuracy of the underwater multi-base sound receiving system is improved.
The optimization algorithm includes genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, gradient descent, and the like, which are not particularly limited in this application.
As shown in fig. 2, in an embodiment of the present application, an adaptive genetic algorithm is used to perform iterative optimization on a position coordinate model, so that a nonlinear problem can be better handled and a problem of a local optimal solution can be avoided, thereby searching and optimizing the position coordinate model more efficiently.
Specifically, step S3 specifically includes:
s31, setting W initial base sound position coordinates according to constraint conditions, wherein the initial base sound position coordinates are used as individuals of the first population. That is, the first population includes W individuals, each of which may be considered as a two-dimensional variable of the base sound location coordinates.
In one embodiment of the present application, a first population of individuals is generated using real codes for initial base sonar location coordinates.
S32, after the parent individuals in the first population are selected pairwise repeatedly, performing cross operation to generate 2W new individuals.
The crossing operation may be single-point crossing, multi-point crossing, etc. to generate new individuals.
Further, the crossover formula adopted by the crossover operation in step S32 is specifically as follows:
wherein z is [0,1]Random real number, X is new individual generated by crossing, X 1 Represents the first parent, X 2 Representing a second parent individual.
According to the method and the device, the combination among different individuals is introduced through the cross operation, so that the diversity of solutions in the first population is increased, the problem that the underwater multi-base sound system falls into local optimum and finally the iteration speed is too low is solved, and the global searching capability of the underwater multi-base sound system is improved.
S33, the 2W new individuals form a coverage area of an underwater multi-base sonar system, and the fitness value of the base sonar is determined according to the coverage area of the underwater multi-base sonar system.
It can be understood that the fitness value is a mathematical function for evaluating the merits of individuals, and the effective coverage area of the underwater multi-base sound receiving system is evaluated through the fitness value.
In the embodiment of the present application, a monte carlo method is adopted to calculate the ratio of the actual point M to the total extraction point M, that is, the fitness value of 2W new individuals obtained by intersecting in step S32 needs to be calculated.
As will be appreciated by those skilled in the art, the monte carlo method can approximately calculate the solution or probability distribution of a problem from a large number of random samples. In the embodiment of the application, the Monte Carlo method is adopted to calculate the fitness value of the underwater multi-base sonar system, so that the positioning accuracy of the underwater multi-base sonar system is improved.
S34, arranging the fitness values in descending order according to the order from large to small, and selecting the first W individuals in the fitness values to form a second population;
s35, performing mutation operation on the second population to obtain a third population.
In the embodiment of the application, a new population is generated through mutation operation, and information in a second population is prevented from being missed, wherein the mutation operation adopts a mode of adding random numbers to individuals in the second population, namely adding the random numbers to the abscissa and the ordinate of the original individuals, so that mutation operation is further carried out, differentiation among the individuals is promoted, the local search process is improved, and the global search capability of the underwater multi-base sound receiving system is improved.
S36, repeating the steps S32-S35 for M times to perform iterative optimization, and stopping iterative optimization when M meets the termination condition, and outputting the actual position coordinates of the underwater multi-base sound receiving system.
In the embodiment of the present application, the optimization algorithm is used to iterate the position coordinate model, and the mutation operation is used to continuously update the population information, so as to obtain the optimal spatial array structure, where the termination condition can be set according to the actual requirement, and the application does not limit the setting, and M is a non-zero natural number.
In an embodiment of the present application, the spatial array position optimization method further includes the steps of:
and S4, optimizing the array position of the underwater multi-base sound system according to the actual position coordinates of the underwater multi-base sound system to obtain an optimal array position node of the underwater multi-base sound system.
According to the method, the actual position coordinates of the underwater multi-base sonar system are used for optimizing the array position of the sonar system, so that the optimal structure of the sonar system can be obtained, the maximum coverage of the underwater multi-base sonar system is achieved, the utilization rate of the sonar is improved, and the cost is reduced.
In an embodiment of the application, an adaptive genetic algorithm is adopted to optimize the array position of the underwater multi-base sound receiving system, the variation probability of the group is reconstructed according to the optimization process, the variation probability is reduced along with the increase of the adaptability of the group, the running speed of the space array position optimization method is improved, the iteration speed is prevented from being too slow, and the iteration solving speed is increased.
Specifically, step S4 specifically includes:
the actual position of the underwater multi-base sound receiving system forms a target radiation area, and the actual position coordinates are subjected to cross operation to generate a fourth population;
calculating the fitness value of the individuals in the fourth population, and generating an objective function according to the fitness value;
calculating error geometric distribution of each vertex in the target radiation area, and arranging the error geometric distribution of each vertex in a descending order;
performing variation operation on the error geometric distribution of each vertex to obtain variation probability;
calculating a weight coefficient of variation probability according to the fitness value of the individuals in the fourth population;
and optimizing the array position of the underwater multi-base sound receiving system according to the weight coefficient to obtain an optimal array position node of the underwater multi-base sound receiving system.
In the embodiment of the application, the coverage area of the underwater multi-base sonar system is processed in a grid discretization processing mode, namely, the weighted average of error geometric distribution values (GDOP values) of the underwater multi-base sonar in the coverage area is solved, the calculated amount of a coordinate model is reduced, and therefore the optimal position distribution of the underwater multi-base is solved in an accelerating mode.
Further, the variation probability P m The formula of (2) is:
i=1，2，…，W；
wherein k is 1 And k 2 Represents a random real number on (0, 1), W represents the number of the population, f i Indicating the fitness value of the ith individual in the population, f max Representing the maximum fitness value of the individuals in the population,mean value of fitness values representing all individuals in the population, +.>A weight coefficient representing a variation probability, wherein the weight coefficient of the variation probability is a normalized coefficient on (0, 1).
Further, the weight coefficient of the variation probabilityThe method comprises the following steps:
j=1, 2, …, W and i+.j;
wherein f j Represents the jthFitness value of the population.
In the embodiment of the application, the weight coefficient of the variation probabilityThe difference between the individual fitness value and the population fitness value in the population is reflected, and the larger the weight coefficient of the variation probability is, the higher the deviation between the individual and the population is represented, so that the progress of the actual position coordinate to the matrix optimization is changed by adjusting the variation probability.
In the embodiment of the application, the constraint conditions are established according to the characteristics of sonar, wherein the constraint conditions comprise an effective detection range of sonar, avoidance of dead zones, minimization of cost and the like, the position coordinate model can meet actual use requirements by considering the influence of the constraint conditions in the process of optimizing the position coordinate model, the position coordinate model is adapted to complex environmental conditions, and meanwhile, the robustness of positioning of the underwater multi-base sonar system can be enhanced by iterative optimization of the position coordinate model.
According to the method, the position coordinates of the underwater multi-base sound receiving system are solved through the self-adaptive genetic algorithm and the Monte Carlo method and the K/N fusion criterion, wherein the genetic algorithm can quickly search and find the optimal solution, meanwhile, the genetic algorithm also has the characteristic of global optimization, the problem of local optimal solution can be avoided, the complex nonlinear problem can be processed to a certain extent through the combination of the genetic algorithm and the Monte Carlo method, the optimal base position coordinates can be determined more accurately through the combination of the K/N fusion criterion, and therefore the performance and the accuracy of the underwater multi-base sound receiving system are improved.
The application also provides a space array position optimizing system of the underwater multi-base sound receiving system, which adopts the space array position optimizing method of the underwater multi-base sound receiving system, comprising the following steps:
the constraint module is used for establishing constraint conditions according to the characteristics of the underwater multi-base sonar system and determining the coverage range of the underwater multi-base sonar system;
the first optimization module is used for establishing an optimization function according to the constraint condition and the coverage range of the underwater multi-base sound receiving system, and adjusting the optimization function to establish a position coordinate model;
and the iteration module is used for setting initial base sound accommodation position coordinates according to the constraint conditions, carrying out iteration optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound accommodation system.
In the embodiment of the application, an effective constraint condition is established through the constraint module, so that the coverage range of the underwater multi-base sonar system is determined, the position of each underwater base sonar is ensured to be in a reasonable range of practical application, and the performance and the stability of the space array position optimization system are ensured; and establishing an optimization function through a first optimization module, and adjusting the optimization function to establish a position coordinate model, and introducing actual position coordinates in the optimization process, so that the optimization process is more accurate and realistic.
In a further embodiment of the present application, the spatial array position optimization system further includes a second optimization module, configured to optimize the array position of the underwater multi-base sound system according to the actual position coordinates of the underwater multi-base sound system, so as to obtain an optimal array position node of the underwater multi-base sound system.
In the embodiment of the application, the actual position coordinates of the underwater multi-base sonar system are optimized through the second optimization module, so that the optimal array node of the underwater multi-base sonar system is obtained, the overall performance of the underwater multi-base sonar system is enhanced, the energy loss in the sonar signal transmission process is reduced, the energy consumption of the space array optimizing system is degraded, the optimizing result has practical significance and robustness, the influence of errors and deviations generated in the sonar system installation or layout process is avoided, and the robustness of the space array optimizing system are improved.
The application also provides an electronic device comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement a method of optimizing spatial array bits of an underwater multi-base sound absorption system as described in any of the above.
The application also provides a computer readable storage medium storing computer instructions that cause the computer to implement a method for optimizing a spatial array position of an underwater multi-base sound absorption system according to any one of the above.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (5)
1. The space array position optimizing method of the underwater multi-base sound receiving system is characterized by comprising the following steps of:
s1, establishing constraint conditions according to characteristics of a sonar system, and determining coverage of the underwater multi-base sonar system;
s2, establishing a position coordinate model according to the constraint conditions and the coverage range of the underwater multi-base sound receiving system;
s3, setting initial base sound receiving position coordinates according to the constraint conditions, performing iterative optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound receiving system;
the step S2 specifically comprises the following steps:
determining a measurement mode of the underwater multi-base sound system according to the coverage range of the underwater multi-base sound system;
establishing an optimization function by combining the coverage range, the measurement mode and the constraint condition of the underwater multi-base sound receiving system;
adjusting the optimization function, and establishing a position coordinate model according to the adjusted optimization function;
the space array position optimizing method further comprises the steps of:
s4, optimizing the array position of the underwater multi-base sound system according to the actual position coordinates of the underwater multi-base sound system to obtain an optimal array position node of the underwater multi-base sound system;
the step S4 specifically comprises the following steps:
the actual position of the underwater multi-base sound receiving system forms a target radiation area, and the actual position coordinates are subjected to cross operation to generate a fourth population;
calculating the fitness value of the individuals in the fourth population, and generating an objective function according to the fitness value;
calculating error geometric distribution of each vertex in the target radiation area, and arranging the error geometric distribution of each vertex in a descending order;
performing variation operation on the error geometric distribution of each vertex to obtain variation probability;
calculating a weight coefficient of variation probability according to the fitness value of the individuals in the fourth population;
optimizing the array position of the underwater multi-base sound receiving system according to the weight coefficient to obtain an optimal array position node of the underwater multi-base sound receiving system;
the mutation probability P m The formula of (2) is:
；i=1,2，…，W；
wherein k is 1 And k 2 Represents a random real number on (0, 1), W represents the number of the population, f i Indicating the fitness value of the ith individual in the population, f max Representing the maximum fitness value of the individuals in the population,representing the average of all individual fitness values in the population,a weight coefficient representing a variation probability, wherein the weight coefficient of the variation probability is a normalized coefficient on (0, 1);
weight coefficient of the variation probabilityThe method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the j=1, 2, …, W and i+.j;
wherein f j And the fitness value of the jth population is represented.
2. The method for optimizing the spatial array position of an underwater multi-base sound system according to claim 1, wherein the formula of the optimizing function is as follows:
；
wherein u is 0 And (3) representing a fusion detection result of the underwater multi-base sonar system, wherein K represents the number of times the sonar system detects a target, N represents the number of times of judging the result, and f (u) represents the result of the optimization function.
3. The method for optimizing the space array position of an underwater multi-base sound system according to claim 1, wherein the step S3 specifically comprises:
s31, setting W initial base sound position coordinates according to constraint conditions, wherein the initial base sound position coordinates are used as individuals of a first population;
s32, after the parent individuals in the first population are selected pairwise repeatedly, performing cross operation to generate 2W new individuals;
s33, the 2W new individuals form a coverage area of an underwater multi-base sonar system, and an adaptability value of the base sonar is determined according to the coverage area of the underwater multi-base sonar system;
s34, arranging the fitness values in descending order according to the order from large to small, and selecting the first W individuals in the fitness values to form a second population;
s35, performing mutation operation on the second population to obtain a third population;
s36, repeating the steps S32-S35 for M times to perform iterative optimization, and stopping iterative optimization when M meets the termination condition, and outputting the actual position coordinates of the underwater multi-base sound receiving system.
4. A method for optimizing the spatial array position of an underwater multi-base sound system as claimed in claim 3, wherein the cross operation in step S32 adopts a cross formula as follows:
；
wherein z is [0,1]Random real number, X is new individual generated by crossing, X 1 Represents the first parent, X 2 Representing a second parent individual.
5. A space array position optimizing system of an underwater multi-base sound system, characterized in that the space array position optimizing method of the underwater multi-base sound system as claimed in any one of claims 1 to 4 is adopted, comprising:
the constraint module is used for establishing constraint conditions according to the characteristics of the underwater multi-base sonar system and determining the coverage range of the underwater multi-base sonar system;
the first optimization module is used for establishing an optimization function according to the constraint condition and the coverage range of the underwater multi-base sound receiving system, and adjusting the optimization function to establish a position coordinate model;
and the iteration module is used for setting initial base sound accommodation position coordinates according to the constraint conditions, carrying out iteration optimization on the position coordinate model by using an optimization algorithm, and outputting actual position coordinates of the underwater multi-base sound accommodation system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311254865.6A CN117010132B (en) | 2023-09-27 | 2023-09-27 | Space array position optimization method and system of underwater multi-base sound system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311254865.6A CN117010132B (en) | 2023-09-27 | 2023-09-27 | Space array position optimization method and system of underwater multi-base sound system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117010132A CN117010132A (en) | 2023-11-07 |
| CN117010132B true CN117010132B (en) | 2023-12-26 |
Family
ID=88576534
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311254865.6A Active CN117010132B (en) | 2023-09-27 | 2023-09-27 | Space array position optimization method and system of underwater multi-base sound system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117010132B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117849807B (en) * | 2024-03-06 | 2024-05-10 | 西北工业大学青岛研究院 | Method for optimizing tripwire sonar node layout of forward scattering detection |
| CN118169695B (en) * | 2024-03-08 | 2024-10-18 | 哈尔滨工程大学 | Array position optimization method of submarine heterogeneous sonar detection system in deep sea environment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105572658A (en) * | 2016-01-19 | 2016-05-11 | 苏州桑泰海洋仪器研发有限责任公司 | Three-dimensional imaging sonar reception plane array element sparse optimization method based on modified Genetic algorithm |
| CN109633555A (en) * | 2019-02-28 | 2019-04-16 | 哈尔滨理工大学 | Submarine target localization method based on hereditary MUSIC algorithm |
| CN109635486A (en) * | 2018-12-20 | 2019-04-16 | 华中科技大学 | A kind of high resolution three-dimensional imaging sonar transducer array sparse optimization method |
| WO2019237621A1 (en) * | 2018-06-14 | 2019-12-19 | 浙江大学 | Sparse optimization method based on cross-shaped three-dimensional imaging sonar array |
| CN112580265A (en) * | 2020-12-29 | 2021-03-30 | 南京航空航天大学 | High-precision aerospace shuttle aircraft pressure measuring hole layout method |
| WO2022000924A1 (en) * | 2020-07-01 | 2022-01-06 | 北京工业大学 | Double-resource die job shop scheduling optimization method based on ammas-ga nested algorithm |
| CN115563749A (en) * | 2022-09-13 | 2023-01-03 | 中国人民解放军国防科技大学 | Method, system and medium for optimizing passive sonar buoy arrangement |
| CN116804747A (en) * | 2023-06-26 | 2023-09-26 | 中国人民解放军国防科技大学 | Passive sonar buoy array method and device |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7738318B2 (en) * | 2006-11-29 | 2010-06-15 | Lockheed Martin Corporation | Method and apparatus for fault-tolerant, correlation SONAR processing |
| CN104077496A (en) * | 2014-07-17 | 2014-10-01 | 中国科学院自动化研究所 | Intelligent pipeline arrangement optimization method and system based on differential evolution algorithm |
| CN105405118B (en) * | 2015-10-16 | 2017-11-21 | 哈尔滨工程大学 | The underwater sonar image object detection method to be leapfroged based on quantum derivative mixing |
| CN109656136B (en) * | 2018-12-14 | 2022-03-18 | 哈尔滨工程大学 | Underwater multi-AUV (autonomous underwater vehicle) co-location formation topological structure optimization method based on acoustic measurement network |
| CN110602757B (en) * | 2019-09-18 | 2023-05-12 | 上海海事大学 | Wireless sensor network clustering routing method based on adaptive genetic algorithm |
| CN111458698B (en) * | 2020-04-02 | 2022-07-15 | 哈尔滨工程大学 | Passive sonar sparse bit optimization method |
| CN112287547A (en) * | 2020-10-29 | 2021-01-29 | 西北工业大学 | Passive buoy array optimization method based on NSGA-II |
| CN114329854B (en) * | 2020-11-06 | 2023-05-12 | 北京航空航天大学 | Two-dimensional space vision sensor layout optimization method based on multi-target constraint |
| CN115629389A (en) * | 2022-09-13 | 2023-01-20 | 西北工业大学 | Target positioning and error analysis method based on three-dimensional space multi-base sonar |
| CN115880572A (en) * | 2022-12-19 | 2023-03-31 | 江苏海洋大学 | Forward-looking sonar target identification method based on asynchronous learning factor |
| CN116125386A (en) * | 2023-02-10 | 2023-05-16 | 江苏科技大学 | Intelligent positioning method and system for underwater vehicle with enhanced sparse underwater acoustic ranging |
| CN115952691B (en) * | 2023-03-10 | 2023-06-13 | 南京雷电信息技术有限公司 | Optimal station distribution method and device for multi-station passive time difference cross joint positioning system |
| CN116577763A (en) * | 2023-03-28 | 2023-08-11 | 中国船舶集团有限公司第七一五研究所 | Combined detection method aiming at improving action distance of two heterogeneous nodes |
-
2023
- 2023-09-27 CN CN202311254865.6A patent/CN117010132B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105572658A (en) * | 2016-01-19 | 2016-05-11 | 苏州桑泰海洋仪器研发有限责任公司 | Three-dimensional imaging sonar reception plane array element sparse optimization method based on modified Genetic algorithm |
| WO2019237621A1 (en) * | 2018-06-14 | 2019-12-19 | 浙江大学 | Sparse optimization method based on cross-shaped three-dimensional imaging sonar array |
| CN109635486A (en) * | 2018-12-20 | 2019-04-16 | 华中科技大学 | A kind of high resolution three-dimensional imaging sonar transducer array sparse optimization method |
| CN109633555A (en) * | 2019-02-28 | 2019-04-16 | 哈尔滨理工大学 | Submarine target localization method based on hereditary MUSIC algorithm |
| WO2022000924A1 (en) * | 2020-07-01 | 2022-01-06 | 北京工业大学 | Double-resource die job shop scheduling optimization method based on ammas-ga nested algorithm |
| CN112580265A (en) * | 2020-12-29 | 2021-03-30 | 南京航空航天大学 | High-precision aerospace shuttle aircraft pressure measuring hole layout method |
| CN115563749A (en) * | 2022-09-13 | 2023-01-03 | 中国人民解放军国防科技大学 | Method, system and medium for optimizing passive sonar buoy arrangement |
| CN116804747A (en) * | 2023-06-26 | 2023-09-26 | 中国人民解放军国防科技大学 | Passive sonar buoy array method and device |
Non-Patent Citations (3)
| Title |
|---|
| Gain and Phase Autocalibration of Large Uniform Rectangular Arrays for Underwater 3-D Sonar Imaging Systems;Yuan Longtao;IEEE Journal of Oceanic Engineering(第3期);第458-471页 * |
| 基于二维成像声纳的水下三维重建方案研究;张微之;中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑);C036-379 * |
| 基于线性最小二乘估计的双基地声纳定位优化算法;刘若辰;王英民;张群;;鱼雷技术(第06期);第428-432页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117010132A (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117010132B (en) | Space array position optimization method and system of underwater multi-base sound system | |
| Leung et al. | Detection of small objects in clutter using a GA-RBF neural network | |
| Liu et al. | Underwater target tracking in uncertain multipath ocean environments | |
| Anower | Estimation using cross-correlation in a communications network | |
| Leung | Applying chaos to radar detection in an ocean environment: an experimental study | |
| CN111460597A (en) | Radar station distribution method based on improved multi-target particle swarm optimization algorithm | |
| Blanding et al. | Directed subspace search ML-PDA with application to active sonar tracking | |
| CN109975810A (en) | A kind of immersed body detection method, device and terminal device | |
| CN113687321B (en) | Radar target detection distance evaluation method and device | |
| Avcioglu et al. | Three dimensional volume coverage in multistatic sonar sensor networks | |
| Angley et al. | Non‐myopic sensor scheduling for multistatic sonobuoy fields | |
| Guo et al. | Target depth estimation by deep neural network based on acoustic interference structure in deep water | |
| Ristic et al. | Gaussian mixture multitarget–multisensor Bernoulli tracker for multistatic sonobuoy fields | |
| CN103728608A (en) | Antenna arrangement method for improving MIMO-OTH radar detecting performance in ionized layer double-Gaussian model | |
| Barshandeh et al. | A learning-based metaheuristic administered positioning model for 3D IoT networks | |
| CN101846738B (en) | Visual element positioning method based on interface reflection polarity discrimination | |
| CN109061652B (en) | Detection efficiency evaluation method of underwater acoustic networking detection system | |
| Narykov et al. | Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation | |
| Li et al. | Sparse feature points extraction-based localization with partial information loss in UWSNs | |
| Rogers et al. | Empiric Bayesian Inversion of Evaporation Ducts from Synthetic Phased-Array Data | |
| Blanding et al. | Covert sonar tracking | |
| CN118131244B (en) | Method and device for generating optimal array shape of underwater acoustic positioning of deep sea heterogeneous multi-mobile platform | |
| Peshekhonov et al. | A physical basis for designing integrated acoustic network systems for underwater observations | |
| Weishuai et al. | The Influential Factors and Prediction of Kuroshio Extension Front on Acoustic Propagation-Tracked | |
| da Costa et al. | A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |