CN109506763B - Vector hydrophone sparse array arrangement method based on teaching and learning optimization - Google Patents
Vector hydrophone sparse array arrangement method based on teaching and learning optimization Download PDFInfo
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Abstract
The invention provides a vector hydrophone sparse arraying method based on teaching and learning optimization, which comprises the following steps of: (1) the method comprises the steps of determining a vector hydrophone combined directivity and composite array directivity function, (2) initializing an optimization process, (3) selecting individual teachers, (4) independently learning teachers, (5) teaching teachers, (6) mutually learning students, and (7) terminating judgment. The invention combines the practical classroom teaching process, introduces the teacher self-learning stage, utilizes the working mechanism of the vector hydrophones in the sparse array to switch and optimize in the two states of activation and closing, enhances the local searching capability of teaching and learning optimization, can obtain better sparse array optimization results, does not need to adjust the specific parameter factors of the algorithm, saves a large amount of algorithm adjusting time, and better accords with the practical engineering application.
Description
Technical Field
The invention belongs to the technical field of sonar, and particularly relates to a sparse array arrangement method of vector hydrophones based on teaching and learning optimization.
Background
The vector hydrophone is used as a novel underwater sound measuring sensor, is structurally formed by compounding a traditional nondirectional sound pressure sensor and a dipole directional vibration velocity sensor, can synchronously and jointly measure sound pressure and vibration velocity information, fundamentally solves the problem of port and starboard fuzzy, and is widely applied to the fields of underwater sound warning sonar, towed line array sonar, shipboard array conformal sonar, multi-base sonar and the like.
The sparse array is an array with array elements distributed in a sparse form obtained by removing part of array elements from a traditional densely-arranged full array on the basis of meeting array performance constraint, and can reduce the number of sensors and reduce the system hardware cost on the premise of obtaining higher resolution. The vector hydrophone sparse array distribution technology is beneficial to solving the problems of invalid array element repair and distributed multi-base sonar array distribution in the actual sonar array, and has important engineering value.
R.V Rao et al propose a teaching and learning optimization algorithm based on the process of sharing and acquiring knowledge of teachers and students in the course of classroom teaching. In the teaching stage, class members learn according to the difference between the teacher matrix and the average level of students; in the learning stage, the members in the class are compared with each other, and the member with low fitness learns from the member with high fitness. The knowledge sharing mode omits the autonomous learning stage of the teacher, and generally speaking, the teacher is the member with the highest fitness in the whole population, and needs to spend a large amount of time for autonomous learning to prepare teaching contents before giving lessons in a classroom so as to improve the classroom teaching quality.
The existing sparse array arrangement technology generally adopts a random optimization algorithm to search for a global optimal solution of the problems, such as a simulated annealing algorithm, a genetic algorithm, a particle swarm algorithm and the like, and the random optimization algorithm has a common problem that the adjustment of specific parameter factors of the algorithm is very complicated, such as a cooling coefficient used for controlling the temperature drop speed in the simulated annealing algorithm, a variation factor and a cross factor in the genetic algorithm, and a learning factor and an inertia weight in the particle swarm algorithm. Such parameter factors require a lot of time to adjust in the algorithm implementation process, and once the problem model or application occasion changes, the algorithm parameters need to be adjusted again to adapt to a new problem model or application occasion, which is very unfavorable for practical engineering application.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a vector hydrophone sparse array arrangement method based on teaching and learning optimization, which mainly comprises the following steps:
(1) determining a vector hydrophone combination directivity and composite array directivity function:
the sound pressure and vibration speed channel output signals of a single vector hydrophone are utilized to obtain and combine to form a directivity function R (f, theta), wherein f is the center frequency of the hydrophone, and theta is the horizontal azimuth angle. The sound pressure signal is recorded as p, the vibration velocity signal is recorded as v, and the vibration velocity sensors can form dipole directivity in a three-dimensional space. In the ocean waveguide, since a vertical direction is a standing wave, two-dimensional directivity in a horizontal direction is generally considered.
Determining a composite array directivity function F consisting of a plurality of vector hydrophones according to the combined directivity function R (F, theta) of the single vector hydrophonem(f, θ) expressed by
Fm(f，θ)＝R(f，θ)·F(f，θ)
Where F (F, θ) is the directivity function of the scalar array of sound pressures.
(2) Initializing an optimization process:
control parameters in the teaching and learning optimization algorithm are initialized, and the control parameters comprise the number Pz of the class individuals and the maximum iteration number Gen. And initializing and randomly assigning excitation weight values w of the vector hydrophone array, wherein the random excitation weight values are random real numbers between 0 and 1, the matrix scale is PzxM, and M represents the number of vector hydrophones in the array. The excitation weight value is indiscriminately acted on all sound pressure and vibration velocity channels of the vector hydrophone, namely the sound pressure and the excitation weight value on the vibration velocity channel in a single vector hydrophone are the same.
Defining an ith class individual matrix wiCorresponding objective function f (w)i) Is expressed as
||·||0Is represented by 10Norm ofOperation for measuring the sparsity, k, of the matrix1The penalty coefficient is used for controlling the peak level of the side lobe, and the value size of the penalty coefficient is related to the number of the beams in the whole visible area and the resolution ratio of the beams; s denotes the region where the beam energy value is higher than the desired sidelobe peak.
(3) Selecting individual teacher step
Evaluating the individual fitness of the class, selecting the individual with the best fitness as the individual of the teacher, wherein the fitness function of the class can be expressed as
Where g represents the current corresponding number of iterations. Selecting the member with the highest fitness in the class individuals as the teacher
The remaining class members are students.
(4) Teacher self-learning step
The teacher prepares classroom teaching contents through autonomous learning, the autonomous learning is developed by adopting a local search method, and the autonomous learning stage of the teacher comprises the following steps:
(4.1) selecting the mth vector hydrophone according to the random traversal sequence, and judging the excitation weight w of the sound pressure and vibration velocity channels of the vector hydrophonest，mIf the value is 0, starting an 'activation' process if the value is 0, and otherwise, starting a 'shutdown' process.
The "activation" process includes the steps of:
(4.1.1) assigning a value to the vector hydrophone by using a random excitation weight, wherein the random excitation weight is a real number between 0 and 1, and the excitation weight acts on all sound pressure and vibration velocity channels of the vector hydrophone.
(4.1.2) judging whether the new teacher matrix can obtain better fitness after being assigned, if so, receiving a new excitation weight, and otherwise, resetting all the excitation weights of the sound pressure and vibration velocity channels of the vector hydrophone to be 0.
The "shutdown" procedure includes the steps of:
and (4.2.1) setting the excitation weight of all sound pressure and vibration velocity channels of the vector hydrophone to be 0.
(4.2.2) judging whether the new teacher matrix can obtain better fitness after being assigned, if so, receiving a new excitation weight, and otherwise, returning all the excitation weights of the sound pressure and vibration velocity channels of the vector hydrophone to the original state.
(4.2) judging whether all vector hydrophones in the teacher individual traverse, if so, ending the self-learning step of the teacher, and otherwise, turning to the step (4.1).
(5) Teaching stage steps of teachers
In the stage, the teacher gives lessons according to the overall knowledge level of the class members, and the aim is to enable the class average level to be closer to the knowledge level of the teacher. The difference between the teacher and the average knowledge level of the class is defined as follows:
wherein rand represents [0, 1 ]]Random number of (a), representing the average knowledge level of the class, TfAnd expressing a teaching factor, wherein the factor is used for expressing a teaching effect and takes the value of 1 or 2 with equal probability. The class member knowledge updating mode at the stage is
If the updated value of the member individual
Better results than originally achieved are retained, otherwise the original stimulus state is returned.
(6) The step of mutual learning stage for students
(6.1) randomly selecting two student individual weight matrixes
And
the corresponding objective function values are respectively
And
if it is
I.e. the fitness of the individual i is higher than that of the individual j, at the moment, the individual j learns from the individual i,
otherwise the individual i learns from the individual j,
(6.2) if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing; if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing;
(6.3) repeating the steps (6.1) - (6.2) to make the multiple class individuals have an opportunity to learn each other, wherein the number of times of repeated execution is determined by the number of the class individuals.
(7) Termination judgment step
If the current iteration times g reach the maximum iteration times Gen, terminating the optimization process, outputting a final class excitation weight matrix, and selecting the optimal individual as a final vector hydrophone sparse array scheme; otherwise, go to step (3).
Compared with the prior art, the invention has the following advantages:
(1) the vector sparse array arrangement method provided by the invention does not need to adjust specific parameter factors of the algorithm, and can execute the sparse array arrangement optimization process by only setting the population scale and the maximum iteration number of the algorithm, thereby saving a large amount of algorithm adjustment time and being more in line with the practical application of engineering.
(2) The invention combines the practical classroom teaching process, introduces the teacher self-learning stage, utilizes the working mechanism of the vector hydrophones in the sparse array to switch and optimize in the states of activation and closing, enhances the local searching capability of teaching and learning optimization, and can obtain better sparse array optimization results.
Drawings
FIG. 1 is a flow chart of the sparse arraying method of vector hydrophones according to the present invention;
FIG. 2 is a diagram showing a vector hydrophone array excitation weight distribution after sparse arrangement;
fig. 3 is a diagram of a sparse vector hydrophone array versus the original array beam.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a flow chart of a sparse arraying method for vector hydrophones based on teaching and learning optimization, based on which, the sparse arraying is performed on the most common linear arrays in the sonar field, and the main steps are as follows:
(1) determining a vector hydrophone combination directivity and composite array directivity function:
obtaining a directional function R (f, theta) formed by combining sound pressure and vibration speed channel output signals of a single vector hydrophone, wherein f is the center frequency of the vector hydrophone, theta is the horizontal azimuth angle, the sound pressure signal is recorded as p, the vibration speed signal is recorded as v, and the combined directivity is
In the formula
To guide orientation, vcFor combining the vibration velocities, from two orthogonal vibration velocity components vxAnd vyIs formed by combining the following formula
In the example, the most common half-wavelength uniform linear array in the sonar field is considered, the number of array elements is 32, the central frequency of the vector hydrophone array is 8kHz, and the focusing direction of the main beam is 90 degrees.
Determining a composite array directivity function F consisting of a plurality of vector hydrophones according to the combined directivity function R (F, theta) of the single vector hydrophonem(f, θ) expressed by
Fm(f，θ)＝R(f，θ)·F(f，θ)
Wherein F (F, theta) is a directivity function of a sound pressure scalar array and is expressed as
(2) Initializing an optimization process:
control parameters in the teaching and learning optimization algorithm are initialized, and the control parameters comprise the number Pz of the class individuals and the maximum iteration number Gen. The number of individual classes in this example is set to 50 and the maximum number of iterations is 100.
Initializing and randomly assigning an excitation weight w of the vector hydrophone array, wherein the random excitation weight is a random real number between 0 and 1, the matrix scale is 50 multiplied by 32, and the excitation weight is indiscriminately acted on all sound pressure and vibration speed channels of the vector hydrophone, namely the sound pressure in a single vector hydrophone and the excitation weight on the vibration speed channel are the same.
Defining an ith class individual matrix wiCorresponding objective function f (w)i) Is expressed as
k1The penalty coefficient is used for controlling the peak level of the side lobe, and the value size of the penalty coefficient is related to the number of the beams in the whole visible area and the resolution ratio of the beams; s denotes the region where the beam energy value is higher than the desired sidelobe peak. The penalty factor is set to k in this example10.5, the side lobe region is set to [0, 86 °]∪[94°，360°]。
(3) Selecting individual teacher step
Evaluating the individual fitness of the class, selecting the individual with the best fitness as the individual of the teacher, wherein the fitness function of the class can be expressed as
Where g represents the current corresponding number of iterations. Selecting the member with the highest fitness in the class individuals as the teacher
The remaining class members are students.
(4) Teacher self-learning step
The teacher prepares classroom teaching contents through autonomous learning, the autonomous learning is developed by adopting a local search method, and the autonomous learning stage of the teacher comprises the following steps:
(4.1) selecting the mth vector hydrophone according to the random traversal sequence, and judging the excitation weight w of the sound pressure and vibration velocity channels of the vector hydrophonest，mIf the value is 0, starting an 'activation' process if the value is 0, and otherwise, starting a 'shutdown' process.
The "activation" process includes the steps of:
(4.1.1) assigning a value to the vector hydrophone by using a random excitation weight, wherein the random excitation weight is a real number between 0 and 1, and the excitation weight acts on all sound pressure and vibration velocity channels of the vector hydrophone.
(4.1.2) judging whether the new teacher matrix can obtain better fitness after being assigned, if so, receiving a new excitation weight, and otherwise, resetting the excitation weights of all the sound pressure and vibration velocity channels of the vector hydrophone to be 0.
The "shutdown" procedure includes the steps of:
and (4.2.1) setting the excitation weight of all sound pressure and vibration velocity channels of the vector hydrophone to be 0.
(4.2.2) judging whether the new teacher matrix can obtain better fitness after being assigned, if so, receiving a new excitation weight, and otherwise, returning all the excitation weights of the sound pressure and vibration velocity channels of the vector hydrophone to the original state.
(4.2) judging whether all vector hydrophones in the teacher individual traverse, if so, ending the self-learning step of the teacher, and otherwise, turning to the step (4.1).
(5) Teaching stage steps of teachers
In the stage, the teacher gives lessons according to the overall knowledge level of the class members, and the aim is to enable the class average level to be closer to the knowledge level of the teacher. The difference between the teacher and the average knowledge level of the class is defined as follows:
wherein rand represents [0, 1 ]]Random number of (d), wgRepresenting the average knowledge level, T, of a classfAnd expressing a teaching factor, wherein the factor is used for expressing a teaching effect and takes the value of 1 or 2 with equal probability. The class member knowledge updating mode at the stage is
If the updated value of the member individual
Compared with the original method, the method obtains better resultsWill be retained otherwise the original stimulus state is returned.
(6) The step of mutual learning stage for students
(6.1) randomly selecting two student individual weight matrixes
And
the corresponding objective function values are respectively
And
if it is
I.e. the fitness of the individual i is higher than that of the individual j, at the moment, the individual j learns from the individual i,
otherwise the individual i learns from the individual j,
(6.2) if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing; if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing;
(6.3) repeating the steps (6.1) - (6.2) to make the multiple class individuals have an opportunity to learn each other, wherein the number of times of repeated execution is determined by the number of the class individuals.
(7) Termination judgment step
If the current iteration times g reach the maximum iteration times 100, terminating the optimization process, outputting a final class excitation weight matrix, and selecting the optimal individual as a final vector hydrophone sparse array scheme; otherwise, go to step (3).
The number of hydrophones in the sparse vector array finally obtained in the present example is 24, the excitation weight distribution is shown in fig. 2, and fig. 3 is a spatial beam contrast diagram of the sparse vector array and the original array. According to the final sparse array arrangement result, the sparse vector array has the advantages that the number of hydrophones is reduced by 25% compared with the original array, the maximum side lobe peak value is reduced to-18 dB from the original-13.3 dB, and the better side lobe suppression effect is achieved.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (2)
1. A vector hydrophone sparse arraying method based on teaching and learning optimization is characterized by comprising the following steps:
(1) determining a vector hydrophone combination directivity and composite array directivity function:
utilizing the sound pressure and vibration speed channel output signals of a single vector hydrophone to obtain and combine to form a directivity function R (f, theta), wherein f is the hydrophone center frequency, and theta is a horizontal azimuth angle; the sound pressure signal is recorded as p, the vibration velocity signal is recorded as v, and the vibration velocity sensor can form dipole directivity in a three-dimensional space; in the ocean waveguide, since the vertical direction is a standing wave, the two-dimensional directivity in the horizontal direction is generally considered;
determining a composite array directivity function composed of a plurality of vector hydrophones according to the combined directivity function R (f, theta) of the single vector hydrophoneNumber Fm(f, θ) expressed by
Fm(f，θ)＝R(f，θ)·F(f，θ)
Wherein F (F, θ) is a directivity function of the sound pressure scalar array;
(2) initializing an optimization process:
initializing control parameters in a teaching and learning optimization algorithm, wherein the control parameters comprise the number Pz of class individuals and the maximum iteration number Gen; initializing random assignment to an excitation weight w of the vector hydrophone array, wherein the random excitation weight is a random real number between 0 and 1, and the matrix scale is PzxM, wherein M represents the number of vector hydrophones in the array; the excitation weight value is indiscriminately acted on all sound pressure and vibration velocity channels of the vector hydrophone, namely the sound pressure and the excitation weight value on the vibration velocity channel in a single vector hydrophone are the same;
defining an ith class individual matrix wiCorresponding objective function f (w)i) Is expressed as
||·||0Is represented by0Norm operation for measuring the sparsity, k, of matrix1The penalty coefficient is used for controlling the peak level of the side lobe, and the value size of the penalty coefficient is related to the number of the beams in the whole visible area and the resolution ratio of the beams; s represents the region where the beam energy value is higher than the desired sidelobe peak;
(3) selecting individual teacher step
Evaluating the individual fitness of the class, selecting the individual with the best fitness as the individual of the teacher, wherein the fitness function of the class can be expressed as
Wherein g represents the current corresponding iteration number; selecting the member with the highest fitness in the class individuals as the teacher
The rest members of the class are students;
(4) teacher self-learning step
The teacher prepares classroom teaching contents through autonomous learning, the autonomous learning is developed by adopting a local search method, and the autonomous learning stage of the teacher comprises the following steps:
(4.1) selecting the mth vector hydrophone according to the random traversal sequence, and judging the excitation weight w of the sound pressure and vibration velocity channels of the vector hydrophonest，mIf the value is 0, starting an 'activation' process if the value is 0, otherwise, starting a 'closing' process;
(4.2) judging whether all vector hydrophones in the teacher individual traverse, if so, ending the self-learning step of the teacher, otherwise, turning to the step (4.1);
(5) teaching stage steps of teachers
In the stage, a teacher gives lessons according to the overall knowledge level of class members, and the aim is to enable the class average level to be closer to the knowledge level of the teacher; the difference between the teacher and the average knowledge level of the class is defined as follows:
wherein rand represents [0, 1 ]]Random number of (d), wgRepresenting the average knowledge level, T, of a classfExpressing a teaching factor, wherein the factor is used for expressing a teaching effect and takes the value of 1 or 2 with equal probability; the class member knowledge updating mode at the stage is
If the updated value of the member individual
Compared with the original method, the method obtains better results, and is retained, otherwise, the method returns to the original excitation state;
(6) the step of mutual learning stage for students
(6.1) random selection of two schoolsWeight matrix of the living entity
And
the corresponding objective function values are respectively
And
if it is
I.e. the fitness of the individual i is higher than that of the individual j, at the moment, the individual j learns from the individual i,
otherwise the individual i learns from the individual j,
(6.2) if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing; if the updated weight matrix
If the corresponding objective function value is lower than the original value, then order
Otherwise, not processing;
(6.3) repeatedly executing the steps (6.1) - (6.2) to enable a plurality of class individuals to have an opportunity to learn each other, wherein the repeated execution times are determined by the number of the class individuals;
(7) termination judgment step
If the current iteration times g reach the maximum iteration times Gen, terminating the optimization process, outputting a final class excitation weight matrix, and selecting the optimal individual as a final vector hydrophone sparse array scheme; otherwise, go to step (3).
2. The sparse vector hydrophone arraying method based on teaching and learning optimization as claimed in claim 1, wherein the activating process comprises the following steps:
(4.1.1) assigning a value to the vector hydrophone by using a random excitation weight, wherein the random excitation weight is a real number between 0 and 1, and the excitation weight acts on all sound pressure and vibration velocity channels of the vector hydrophone;
(4.1.2) judging whether the new teacher matrix can obtain better fitness after assignment, if so, receiving a new excitation weight, and otherwise, resetting the excitation weights of all the sound pressure and vibration velocity channels of the vector hydrophone to be 0;
the "shutdown" procedure includes the steps of:
(4.2.1) setting excitation weights of all sound pressure and vibration velocity channels of the vector hydrophone to be 0;
(4.2.2) judging whether the new teacher matrix can obtain better fitness after being assigned, if so, receiving a new excitation weight, and otherwise, returning all the excitation weights of the sound pressure and vibration velocity channels of the vector hydrophone to the original state.
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