CN105842702B - Side lobe suppression method, the array Sparse methods of Multi-beam Imaging Sonar - Google Patents
Side lobe suppression method, the array Sparse methods of Multi-beam Imaging Sonar Download PDFInfo
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Abstract
The invention discloses a kind of Multi-beam Imaging Sonar array side lobe suppression method, it is applied to the optimal element position of all beam directions by wind Drive Optimization algorithm search, simultaneously, optimal weighting coefficientses suitable for each beam direction are calculated by convex optimization method, array secondary lobe is suppressed, the possibility that searching process is absorbed in local optimum is considerably reduced, Sidelobe Suppression effect is more notable；Also disclose a kind of array Sparse methods of Multi-beam Imaging Sonar, utilize the iteration Sparse methods based on hybrid algorithm, on the premise of given main side lobe performance requirement, suppress the side lobe peak of multi-beam level by optimizing element position distribution and weight coefficient, realize that array is sparse on the premise of directional diagram performance loss is little, there is good engineering practicability.
Description
Technical Field
The invention belongs to the field of signal processing in sonar technology, and particularly relates to a side lobe suppression method and an array sparse method of a multi-beam imaging sonar.
Background
In order to obtain high imaging resolution, an imaging sonar generally includes hundreds to thousands of transducer units, each unit corresponds to a conditioning channel, and includes a preamplifier circuit, a TVG (Time variable gain)/AGC (Auto gain control) amplifier circuit, and a filter and acquisition circuit, and the system hardware complexity is very high, and the cost and power consumption are very high.
In the sparse array design, a part of array elements are removed from the full vector of the receiving transducer, and the position and the weight of the reserved transducer are optimally designed again, so that the method is an effective solution for reducing the complexity of system hardware and reducing the cost. However, array sparsity usually causes increase of main lobe width and side lobe peak value of a beam directional diagram, and existing sparse array optimization design schemes are mostly directed at single-beam conditions, and are not applicable to multi-beam imaging sonar systems, and multi-beam requirements greatly increase sparsity difficulty, and are difficult to solve by existing sparse schemes.
Therefore, it is necessary to provide an array sparse method suitable for the multi-beam imaging sonar system under the given main side lobe requirement.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the sidelobe suppression method of the multi-beam imaging sonar is provided, and the problem of sidelobe increase in multi-beam array sparsity in the prior art is solved.
The invention adopts the following technical scheme for solving the technical problems:
a multi-beam imaging sonar array sidelobe suppression method searches for the optimal array element position suitable for all beam directions through a wind-driven optimization algorithm, and meanwhile, calculates the optimal weighting coefficient suitable for each beam direction through a convex optimization method to suppress array sidelobes, and the specific process is as follows:
step a, setting initialization parameters including array element number, particle population size, maximum updating times and sidelobe constraint values;
step b, initializing a particle population, and taking the array element position as an optimization variable;
step c, updating the speed and position vectors of the population particles;
d, obtaining the optimal weight under the layout of each particle array through a convex optimization algorithm;
e, calculating peak side lobe levels of all particle positions, and finding out an optimal solution of the population;
step f, judging whether the peak side lobe level reaches a side lobe constraint value or the updating frequency reaches the maximum, if the peak side lobe level reaches the side lobe constraint value or the updating frequency reaches the maximum, executing the step g, otherwise, repeatedly executing the steps c to f;
and g, taking the peak side lobe level of the optimal position obtained in the step e as a result of array side lobe suppression.
The velocity update formula of the population particles is as follows:
wherein i represents the ascending order of the particles according to the pressure value of the current position, U cur And U new Representing the current velocity and the next generation of particles, respectivelySpeed, X cur Representing the current position of the particle, X opt Which represents the optimal position of the particles,the speed of the particle in the current dimension is influenced by any other dimension, alpha, R and c are constants, and g and T are gravity acceleration and temperature respectively.
The position vector update formula is as follows:
wherein the content of the first and second substances,position vectors X respectively representing next generation particles new And velocity vector U new D =1,2,.., N; rand () represents a random constant distributed between (0,1);representing a Sigmoid function.
And d, converting the weight optimization problem into a convex function form, and solving by adopting an MATLAB toolkit.
The invention also discloses an array sparse method of the multi-beam imaging sonar, and solves the problems that the multi-beam array is difficult to be sparse and no effective method is available for sparse the multi-beam array in the prior art.
In order to solve the technical problem, the following technical scheme is adopted:
the array sparse method of the multi-beam imaging sonar comprises the following steps:
step 1, establishing an array sparse model of a multi-beam imaging sonar array under the given main and side lobe performance requirement;
step 2, optimizing the position and the weighting coefficient of the array element by applying the method of claim 1 under the condition of giving the number of the array elements, and acquiring the peak side lobe level of a plurality of wave beam directions and the optimal position distribution and the weighting coefficient of the array element;
step 3, judging whether the condition of terminating array sparsity of any one of the following (a) and (b) is satisfied or not according to the peak side lobe level acquired in the step 2, if so, executing the step 5, otherwise, executing the step 4,
(a)PSLL r ≤PSLL d and | PSLL r -PSLL d |≤0.001
(b)PSLL r >PSLL d ，
Wherein, PSLL r PSLL for the peak sidelobe level searched in step 2 d For a given side lobe constraint value;
step 4, reducing the number of the array elements by one, and repeatedly executing the step 2 to the step 3;
step 5, judging whether the condition of stopping the array sparseness is (a) or (b), if so, executing step 6, and if so, executing step 7;
step 6, taking the optimal array element position distribution and weighting coefficient obtained under the array element number in the step 2 as the result of the array sparse method;
and 7, increasing the number of the array elements by one, executing the step 2, and taking the optimal array element position distribution and the weighting coefficient obtained under the updated number of the array elements as the result of the array sparse method.
Compared with the prior art, the invention has the following beneficial effects:
1. the array sparsity problem of the multi-beam array under the constraint condition of the main side lobe performance can be processed, and the blank of the array sparsity technology under the multi-beam condition in the prior art is made up.
2. The weight optimization problem is converted into a convex function form which can be simply and conveniently solved by using a MATLAB toolkit, so that the calculation efficiency is improved, and the operation time of the sparse method is effectively reduced.
3. Global optimization and local optimization are respectively carried out by utilizing WDO (Wind driven optimization) and convex optimization, so that the possibility that the optimization process falls into local optimization is greatly reduced, and the sidelobe suppression effect is more remarkable.
4. By using the iterative sparse method based on the hybrid algorithm, the sidelobe peak level of the multi-beam is suppressed by optimizing the position distribution and the weighting coefficient of the array elements on the premise of giving the performance requirement of the main sidelobe, the array sparse is realized on the premise of not losing the performance of the directional diagram, and the method has good engineering practicability.
Drawings
FIG. 1 is a mathematical model diagram of a uniform semicircular array.
FIG. 2 is a flow chart of a hybrid method of WDO and convex optimization algorithms.
Fig. 3 is a graph of simulation results comparing convergence rates of the two methods.
FIG. 4 is a diagram of simulation results of array element position distribution obtained by array sparseness according to the present invention.
Fig. 5 (a) is a picture of imaging the circular ring and the tripod with an initial full array in the anechoic water pool.
Fig. 5 (b) is a picture of imaging a circular ring and a tripod using the sparse array of the present invention in a muffled water pool.
Detailed Description
The structure and operation of the present invention will be further described with reference to the accompanying drawings.
Considering a uniform semicircular array with an array element number N, the array response in the ith beam direction at a signal arrival angle θ is given by:
in the formula (1), the acid-base catalyst,the weighting coefficients for the l-th beam direction on the k-th array element,is the included angle between the connecting line of the kth array element and the array circle center and the x axis of the reference array element,r and λ denote the array radius and signal wavelength, respectively.
In a first embodiment of the present invention, a first,
a multi-beam imaging sonar array side lobe suppression method is characterized in that side lobe suppression of a semicircular array is achieved through a given hybrid algorithm based on wind-driven optimization (WDO) and convex optimization, in the given hybrid algorithm, the WDO algorithm is used as a global optimization algorithm to search a common optimal array element position suitable for all beam directions, and the convex optimization is used as a local optimization algorithm to optimize weighting coefficients of all beam directions. By utilizing WDO and convex optimization to perform global optimization and local optimization respectively, the given hybrid algorithm greatly reduces the possibility that the optimization process falls into local optimization. The hybrid algorithm includes two phases: in the first stage, the WDO algorithm is used for searching the common optimal array element position suitable for all the wave beam directions, in the second stage, the weighting coefficient suitable for each wave beam direction is optimized by a convex optimization method when the pressure function value is calculated, and the array side lobe is restrained, and the specific process is as follows:
step a, setting initialization parameters according to actual conditions, wherein the initialization parameters comprise the number N of multi-beam uniform semicircular array elements and a side lobe constraint value PSLL d And the particle population size in the WDO algorithm, the maximum update times;
step b, initializing a particle population, and taking the array element position as an optimization variable;
step c, updating the speed and position vector of the population particles, wherein the speed updating formula of the population particles is as follows:
wherein i represents the ascending order of the particles according to the pressure value of the current position, U cur And U new Respectively representing the current and next generation velocities, X, of the particle cur Representing the current position of the particle, X opt Which represents the optimal position of the particles,representing the speed of the particle in the current dimension influenced by any other dimension, wherein alpha, R and c are constants, and g and T are gravity acceleration and temperature respectively;
the position vector update formula is as follows:
wherein the content of the first and second substances,position vectors X respectively representing next generation particles new And velocity vector U new D =1,2,.., N; rand () represents a random constant distributed between (0,1);representing a Sigmoid function;
step d, when calculating the pressure function value, converting the weight optimization problem into a form of simply solving a convex function by using a MATLAB toolkit, and acquiring the optimal weight under the layout of each particle array, wherein the form of the convex optimization problem is as follows:
wherein l is the beam number, l =1,2,.., L being the total desired number of beams,is the l-th beam sidelobe region omega l An inner sampling angle, I =1,2,. -, I,the beam pointing for the l-th beam,
after the optimal weight under the particle array layout is obtained, the peak sidelobe levels in a plurality of beam directions are returned as pressure function values, and the form is as follows:
indicating the sidelobe levels in the direction of acquiring a single beam,a function return value representing peak side lobe levels for all beam directions;
e, calculating peak side lobe levels of all particle positions, and finding out the optimal solution of the population;
f, judging whether the peak side lobe level reaches a side lobe constraint value or the updating frequency reaches the maximum, if the peak side lobe level reaches the side lobe constraint value or the updating frequency reaches the maximum, executing the step g, and if not, repeatedly executing the steps c to f;
step g, peak side lobe level PSLL of the optimal position obtained in step e r As a result of array sidelobe suppression.
In a second embodiment of the present invention, a second,
the array sparse method of the multi-beam imaging sonar comprises the following steps:
step 1, establishing an array sparse model of a multi-beam imaging sonar array under the given main and side lobe performance requirement;
the sparseness is realized by extracting partial array elements from a regular grid on the array, the binary number '1' '0' is used for representing the existence of the array elements in the array, and two array elements at two ends of the array are reserved (x) in order to meet the requirement of keeping the width of a main lobe unchanged 1 ＝x N = 1), the sparseness problem is transformed into a mathematical optimization problem in the case of multiple constraints given by equation (2):
in the formula (2), | · non-woven phosphor 0 The expression is taken as a 0 norm; x 1 ＝(x 1 ,x 2 ,...,x N ) T Is a binary representation of the position of the array element, x k =0 denotes no array element at k position, x k =1 denotes an array element at the kth position, k =2,3,. And N-1,x N+1 ,x N+2 ,...x 2N And x 2N+1 ,x 2N+2 ,...x 3N The real part and the imaginary part of the weighting coefficients corresponding to the N array elements, respectively, it should be noted that, for different beam directions, there is a common optimal array element position, but there may be different weighting coefficients; PSLL r PSLL, the peak sidelobe level for the optimal position obtained in step g in the first embodiment d Given side lobe constraint values;
step 2, optimizing the position and the weighting coefficient of the array element by applying the method described in the specific embodiment 1 under the condition of giving the number of the array elements, and acquiring peak side lobe levels in a plurality of beam directions and optimal position distribution and weighting coefficient of the array element;
step 3, judging whether the condition of terminating array sparsity of any one of the following (a) and (b) is satisfied or not according to the peak side lobe level acquired in the step 2, if so, executing the step 5, otherwise, executing the step 4,
(a)PSLL r ≤PSLL d and | PSLL r -PSLL d |≤0.001
(b)PSLL r >PSLL d ，
Wherein, PSLL r PSLL for the peak sidelobe level searched in step 2 d Given side lobe constraint values;
step 4, reducing the number of the array elements by one, and repeatedly executing the step 2 to the step 3;
step 5, judging whether the condition of stopping the array sparseness is (a) or (b), if so, executing step 6, and if so, executing step 7;
step 6, taking the optimal array element position distribution and weighting coefficient obtained under the array element number in the step 2 as the result of the array sparse method;
and 7, increasing the number of the array elements by one, executing the step 2, and taking the optimal array element position distribution and the weighting coefficient obtained under the updated number of the array elements as the result of the array sparse method.
The invention is based on a multi-beam imaging sonar array, aims at an even semicircular array, and inhibits the peak level of a side lobe by a given mixed algorithm based on WDO and convex optimization under the condition of giving main side lobe constraint, thereby realizing the sparse design of the multi-beam array. The WDO algorithm is used as a global optimization algorithm to be optimized and suitable for the common optimal position of all the beams, and the convex optimization is used as a local optimization algorithm to optimize the weighting coefficient of each beam, so that the possibility of falling into local optimization in the optimization process is reduced; secondly, the weight optimization problem of a plurality of wave beams is converted into a convex function form which can be simply solved by using a MATLAB toolkit, so that the calculation efficiency of the algorithm is improved, the time used in the calculation process is shortened, and the method has good engineering practicability.
In the third embodiment of the present invention, in a specific embodiment,
in order to facilitate understanding of the technical solution, the following description will be made in detail by taking an array sparse based on a multi-beam uniform semicircular array as an example.
The semicircular array in this embodiment is composed of N identical array elements 1#,2#, N # distributed uniformly as shown in fig. 1The included angle between the connecting line of the k # and the array circle center and the x axis where the reference array element 1# is positioned isWhere k =1,2. Assuming that the uniform semicircular array has 180 array elements in total, 538 narrow beams are generated between the direction range of 45-135 degrees to realize the scanning of the direction range, and the side lobe constraint value PSLL of the beams d Set to-25 dB. The array radius R is 0.12m and the signal wavelength λ is 0.0033m.
In the WDO algorithm, a population is set to contain 30 particles, and the maximum number of updates is set to 100. The parameter values in the updating process are set as follows: α =0.85, g =0.65, c =0.4, rt =1.5, and the maximum speed is set tod =1,2. The search process is stopped when the found peak sidelobe level meets the constraint or the maximum number of updates is reached.
Firstly, in order to evaluate the sidelobe suppression capability and the computation speed of a given hybrid algorithm (as shown in fig. 2), taking a hybrid method of combining the existing BPSO (Binary vector optimization) with the PSO (Particle swarm optimization) as a reference, when MATLAB simulation comparison is used, the number of fixed array elements is 120 (60 array elements are extracted from a regular grid on an array), and the simulation test result is shown in fig. 3. The convergence curves for both methods are given in fig. 3, with the abscissa and ordinate being the number of updates and the peak side lobe level in dB, respectively.
The result shows that the peak side lobe level of the array can be restrained at-22.1643 dB by the given mixing algorithm and is lower than-16.0519 dB obtained by the existing algorithm; furthermore, the computation time for the given hybrid algorithm is about 10% of the computation time for the existing algorithm in a single update. Therefore, compared with the existing algorithm, the hybrid algorithm has obvious improvement in sidelobe suppression capability and computational efficiency.
Then, the iterative sparse method based on the hybrid algorithm is applied to the sparse design of the sonar array in the embodiment, the number of array elements of the sparse array realized by the design is reduced from 180 under the condition of full array to 151, the array element position distribution is as shown in fig. 4, because the array elements at the two ends are reserved and the array aperture is not changed, the main lobe width of the sparse array is kept unchanged, and meanwhile, the side lobe peak value of the sparse array is suppressed to-25.0091 dB, so that the performance requirement of the main side lobe is met. In order to further illustrate the engineering practicability of the sparse method, imaging performance tests are also respectively carried out on a sparse semicircular array with array element number 151 and an initial full array with array element number 180.
Fig. 5 (a), 5 (b) are pictures of the ring and tripod imaged with the initial full array and the herein obtained sparse array in the anechoic pool, respectively. Compared with the image obtained by the full array, the image obtained by the sparse array has slightly reduced definition, but the target can still be easily identified, and the imaging quality is within an acceptable range.
The iterative sparse method based on the hybrid algorithm is suitable for sparse design of the multi-beam array under the constraint condition of the main and side lobe performance. The algorithm has high calculation efficiency, and the probability of trapping in local optimization in the optimization process is low; the number of array elements can be effectively reduced, and the cost and the power consumption of the multi-beam imaging sonar system are saved. Therefore, the sparse method provided by the method has great engineering practice significance for the array sparse design of the multi-beam imaging sonar system.
Claims (5)
1. A multi-beam imaging sonar array sidelobe suppression method is characterized in that: the method comprises the following steps of searching for the optimal array element position suitable for all wave beam directions through a wind-driven optimization algorithm, meanwhile, calculating the weighting coefficient suitable for each wave beam direction through a convex optimization method, and inhibiting array side lobes, wherein the specific process comprises the following steps:
step a, setting initialization parameters including array element number, particle population size, maximum updating times and sidelobe constraint values;
step b, initializing a particle population, and taking the array element position as an optimization variable;
step c, updating the speed and position vectors of the population particles;
d, obtaining the optimal weight under the layout of each particle array through a convex optimization algorithm;
e, calculating peak side lobe levels of all particle positions, and finding out the optimal solution of the population;
step f, judging whether the peak side lobe level reaches a side lobe constraint value or the updating frequency reaches the maximum, if the peak side lobe level reaches the side lobe constraint value or the updating frequency reaches the maximum, executing the step g, otherwise, repeatedly executing the steps c to f;
and g, taking the peak side lobe level of the optimal position obtained in the step e as a result of array side lobe suppression.
2. The multi-beam imaging sonar array sidelobe suppression method according to claim 1, characterized in that: the velocity update formula of the population particles is as follows:
wherein i represents the ascending order of the particles according to the pressure value of the current position, U cur Representing the current velocity, U, of the particle new Indicates the velocity, X, of the next generation particle cur Representing the current position of the particle, X opt Which represents the optimal position of the particles,the speed of the particle in the current dimension is influenced by any other dimension, alpha, R and c are constants, and g and T are gravity acceleration and temperature respectively.
3. The multi-beam imaging sonar array sidelobe suppression method according to claim 1, characterized in that: the position vector update formula is as follows:
wherein the content of the first and second substances,position vectors X respectively representing next generation particles new And velocity vector U new D =1,2,.., N is the number of array elements; rand () represents a random constant distributed between (0,1);representing a Sigmoid function.
4. The multi-beam imaging sonar array sidelobe suppression method according to claim 1, characterized in that: and d, converting the weight optimization problem into a convex function form, and solving by adopting an MATLAB toolkit.
5. The array sparse method of the multi-beam imaging sonar is characterized in that: the method comprises the following steps:
step 1, establishing an array sparse model of a multi-beam imaging sonar array under the given main and side lobe performance requirement;
step 2, optimizing the position and the weighting coefficient of the array element by applying the method of claim 1 under the condition of giving the number of the array elements, and acquiring the peak side lobe level of a plurality of wave beam directions and the optimal position distribution and the weighting coefficient of the array element;
step 3, judging whether the condition of terminating array sparsity of any one of the following (a) and (b) is satisfied or not according to the peak side lobe level acquired in the step 2, if so, executing the step 5, otherwise, executing the step 4,
(a)PSLL r ≤PSLL d and | PSLL r -PSLL d |≤0.001
(b)PSLL r >PSLL d ，
Wherein, PSLL r PSLL for the peak sidelobe level searched in step 2 d Given side lobe constraint values;
step 4, reducing the number of the array elements by one, and repeatedly executing the step 2 to the step 3;
step 5, judging whether the condition for terminating the array sparsity is (a) or (b), if so, executing step 6, and if so, executing step 7;
step 6, taking the optimal array element position distribution and weighting coefficient obtained under the array element number in the step 2 as the result of the array sparse method;
and 7, increasing the number of the array elements by one, executing the step 2, and taking the optimal array element position distribution and the weighting coefficient obtained under the updated number of the array elements as the result of the array sparse method.
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| CN109959917A (en) * | 2019-03-08 | 2019-07-02 | 南京航空航天大学 | A kind of non-frequency in broadband becomes the array Sparse methods of multi-beam imaging sonar |
| CN110069896B (en) * | 2019-05-29 | 2023-04-07 | 重庆邮电大学 | Vortex electromagnetic wave generation and optimization method based on sparse 2D linear array |
| CN111366918A (en) * | 2020-02-13 | 2020-07-03 | 中国电子科技集团公司第二十九研究所 | Sidelobe cutting method and device |
| CN112083430B (en) * | 2020-09-10 | 2023-11-07 | 天津水聿方舟海洋工程技术有限公司 | Sidelobe suppression method suitable for orbital angular momentum three-dimensional imaging sonar |
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