An optical phased array sidelobe suppression method based on adaptive differential evolution algorithm

Abstract

The application discloses an optical phased array sidelobe suppression method based on an adaptive differential evolution algorithm, and comprises the following steps: S1, initializing a model and setting a termination condition; S2, performing a mutation operation, performing a mutation operation based on an adaptive mutation operator on current population individuals to obtain a population parameter vector Y after mutation i,g+1 (i=1, 2,..., N P ); S3, performing a crossover operation, performing a crossover operation on the parameter vector Y i,g+1 (i=1, 2,..., N P ) after mutation and other target vector parameters X i,g (i=1, 2,..., N P ) to generate a child individual Z i,g+1 (i=1, 2,..., N P ); S4, a boundary condition judgment, judging whether the generated child individual exceeds a pre-set boundary condition; S5, performing a selection operation; and S6, outputting a current optimal individual. Through the improved adaptive differential evolution algorithm, the application optimizes phase modulation parameters of optical phased array split beams, suppresses sidelobe energy of an optical phased array outgoing beam, and thereby improves effective utilization of a far-field light lobe.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optical phased arrays, and particularly relates to an optical phased array sidelobe suppression method based on an adaptive differential evolution algorithm. BACKGROUND

[0002] Optical phased array technology has the advantages of fast response speed, high pointing accuracy, light weight, and small size, and has great development potential in the fields of free space laser communication, solid state laser radar, and long distance three-dimensional imaging. The outgoing beam of the optical phased array is formed by the coherent synthesis of multiple channel beams after they are emitted in space. The far field spot of the phased array is composed of a main lobe and multiple sidelobes. The sidelobes weaken the main lobe energy and affect the overall pointing accuracy of the far field spot. The smaller the sidelobe energy is, the more concentrated the main lobe energy is, and the higher the pointing accuracy of the far field spot is.

[0003] Most of the existing methods use genetic algorithms (Genetic Algorithm) to suppress the sidelobes of optical phased arrays. For example, the patent for invention with publication number CN 112465142 A discloses an optical phased array antenna sidelobe suppression method based on a genetic algorithm framework. It establishes a coordinate-optimized data set by designing a concentric ring arrangement of antenna array sets, randomly extracts initial coordinate positions from the set according to the required number of antennas, uses the genetic algorithm framework to optimize the sidelobe suppression of the optical phased array antenna coordinates, realizes efficient calculation of the fitness function through fast Fourier transform, designs a point-by-point crossover method for chromosome genes to improve optimization efficiency, and uses mutation and other methods to realize a globally optimal light field arrangement scheme to solve the problem of maximizing the main beam energy of the optical phased array antenna. However, this method is mainly used for optimizing the concentric ring arrangement of optical phased array antennas and cannot be applied to one-dimensional linearly arranged optical phased array antennas and other arrangement methods of optical phased array antennas. Moreover, the genetic algorithm takes a long time to run when processing large-scale data, making it difficult to quickly converge to the global optimum.

[0004] Therefore, it is a technical problem that needs to be solved for those skilled in the art to find an algorithm that can suppress the sidelobe energy of one-dimensional linearly arranged and other arranged optical phased arrays, reduce the impact of sidelobes on the pointing accuracy of the far field spot, and quickly converge. SUMMARY

[0005] The purpose of the present application is to provide an optical phased array sidelobe suppression method based on an adaptive differential evolution algorithm, which optimizes the phase modulation parameters of the optical phased array beam splitter, suppresses the sidelobe energy of the optical phased array outgoing beam, and thus improves the effective utilization rate of the far field lobe.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0007] An optical phased array sidelobe suppression method based on an adaptive differential evolution algorithm, comprising the following steps:

[0008] S1, initialize the model and set the termination condition

[0009] Initialize the preset values of the population size, adaptive mutation operator, crossover operator, boundary condition, and take the optical phased array parameter vector as the first generation population parameter vector X i,1 (i=1, 2, …, N P ), and set the termination condition.

[0010] S2, perform mutation operation

[0011] Perform mutation operation on the current population individual based on the adaptive mutation operator to obtain the mutated population parameter vector Y i,g+1 (i=1, 2, …, N P ).

[0012] S3, perform crossover operation

[0013] Crossover the mutated parameter vector Y i,g+1 (i=1, 2, …, N P ) with the parameters X i,g (i=1, 2, …, N P ) of other target vectors to generate child individuals Z i,g+1 (i=1, 2, …, N P ).

[0014] S4, boundary condition judgment

[0015] Determine whether the generated child individual exceeds the pre-set boundary condition, and if not, jump to step S5;

[0016] If the boundary condition is exceeded: if it does not exceed k times the boundary constraint difference (i.e., k(X max -X min )), set the individual value to the adjacent boundary value; if it exceeds k times the boundary constraint difference, replace the individual value with a randomly generated parameter vector in the feasible region.

[0017] S5, perform selection operation

[0018] Bring the newly generated child individual Z i,g+1 (i=1, 2, …, N P ) and the previous generation individual X i,g (i=1, 2, …, N P ) into the sidelobe suppression ratio function Φ(X) = I (X) / I ′ (X) ,(i=1, 2, 3, …, N P), select the individual which makes the sidelobe suppression ratio function value maximum as the next generation; wherein, Φ(X) = I

[0019] I (X) / I ′ (X) is the function mapping relationship of the optical phased array phase modulation parameter vector and the sidelobe suppression ratio, X is the optical phased array phase modulation channel parameter vector, the vector dimension corresponds to the number of optical phased array channels; I (X) is the main lobe peak light intensity, which is the maximum light intensity near the main lobe position; I ′ (X) is the sidelobe peak light intensity, which is the maximum light intensity near the main lobe position after removing the main lobe peak light intensity. In order to determine whether the test vector Z i,g+1 will become a member of the next generation, the differential evolution algorithm compares the test vector with the target vector X i,g in the current population according to the greedy rule. In order to obtain a better sidelobe suppression ratio, the vector with a larger objective function value will appear in the next generation population. All individuals in the next generation are better than or at least as good as the corresponding individuals in the current population.

[0020] S6, loop execution steps S2 to S5, stop after reaching the termination condition, output the current optimal individual, which is the approximate solution of the optimal solution of the model at this time, so as to obtain the parameter vector which makes the sidelobe suppression effect of the optical phased array better.

[0021] Preferably, in step S1, during initialization, it is assumed that all initialized populations meet the uniform probability distribution; the limit of the parameter variable is X jmin <X j <X jmax , then X ji,0 = rand[0, 1]·

[0022] (X jmax -X jmin )+X jmin (i = 1, 2, …, N p ; j = 1, 2, …, d), wherein rand[0, 1] represents a uniform random number generated between [0, 1].

[0023] Preferably, in step S2, for each target vector X ig (i = 1, 2, …, N P ), the mutation vector based on the adaptive differential evolution algorithm is as follows: wherein the randomly selected serial numbers r1, r2, r3 are different from each other, and r1, r2, r3 are also different from the serial number i of the target vector, and must satisfy N P ≥ 4; the adaptive mutation operator F ∈ [0, 2]; the adaptive mutation operator wherein F0 represents a mutation operator, G m represents the maximum evolution generation, G represents the current evolution generation; at the beginning, the adaptive mutation operator is 2F0, which has a large value, and is helpful to maintain the diversity of population individuals and avoid prematureness in the early stage; with the progress of the algorithm, the mutation operator gradually decreases, and the mutation rate approaches F0 in the later stage, thereby preserving good information, avoiding the destruction of the optimal solution, and increasing the probability of the global optimal solution.

[0024] Preferably, in step S3,

[0025]

[0026] wherein randb(j) represents the jth estimation value of a random number generator between [0, 1]; rnbr(i) represents a randomly selected sequence, which is used to ensure that Z i,g+1 At least from Y i,g+1 a parameter is obtained; CR represents a crossover operator, and the value range is [0, 1].

[0027] The concept of the application is:

[0028] The optical phased array controls the composite antenna composed of multiple light channels arranged in a regular manner to make the light beams of each channel coherently synthesized in space, so that the far-field light beam pointing control is achieved. Taking the optical phased array of the optical waveguide as an example, adjusting the phase modulation parameters of each channel of the optical phased array can independently control the phase of the output light of each channel, and change the sidelobe suppression ratio of the far-field light spot of the optical phased array. A real-time monitoring platform for the intensity of the far-field light spot of the optical phased array is built, the peak intensity of the main lobe and the peak intensity of the sidelobe are monitored in real time through an infrared camera, and the corresponding relationship between the phase parameter vector of the optical phased array and the peak intensity of the main lobe and the peak intensity of the sidelobe is obtained. The adaptive differential evolution algorithm is used to continuously iterate the phase parameter vector, and the parameter vector that makes the sidelobe suppression ratio close to the maximum value is solved, so that the sidelobe suppression problem is converted into the problem of solving the global optimal solution by an intelligent optimization method.

[0029] The real-time monitoring process of the far-field light spot intensity of the optical phased array is as follows:

[0030] The adaptive differential evolution algorithm model outputs the current phase modulation parameter vector to the parameter vector issuing module, which synchronously issues the phase modulation parameter vector of each channel to each channel of the optical phased array antenna. The outgoing light beam of the optical phased array is projected on a fixed screen, an infrared camera captures the far-field spot image on the fixed screen and inputs it into a computer, identifies the peak intensity of the main lobe and the peak intensity of the sidelobe of the optical phased array, calculates the sidelobe suppression ratio corresponding to the parameter vector and inputs it into the differential evolution algorithm model.

[0031] Further, the optical phased array sidelobe suppression ratio calculation method is as follows: the infrared camera captures the far-field spot image corresponding to the phase modulation parameter vector X, the point with the maximum light intensity in the spot image is recorded as the main lobe position of the optical phased array, then the maximum light intensity near the point is recorded as the main lobe peak light intensity, that is, I (X) After removing the main lobe peak light intensity, the maximum light intensity near the point is recorded as the sidelobe peak light intensity, that is, I ′ (X) Therefore, the function mapping relationship between the optical phased array phase modulation parameter vector and the optical phased array sidelobe suppression ratio is Φ(X) = I (X) / I ′ (X) Wherein X is the optical phased array phase modulation channel parameter vector, and the vector dimension corresponds to the number of optical phased array channels.

[0032] Compared with the prior art, the present application has the following advantages:

[0033] (1) The model parameters can be quickly converged, the phase modulation parameters of the optical phased array beam splitter are optimized, the sidelobe energy of the optical phased array exit beam is suppressed, and therefore the effective utilization rate of the far-field light lobe and the overall pointing accuracy of the far-field spot are improved.

[0034] (2) The improved adaptive mutation operator is adopted, the individual diversity of the population can be maintained in the early stage, and the prematureness is avoided; the excellent information of the population is reserved in the later stage, the optimal solution is prevented from being destroyed, and the probability of searching the global optimal solution is increased.

[0035] (3) The improved boundary condition processing strategy is adopted, the individual value exceeding the boundary can be more accurately judged, the irrelevant individual value is avoided to be introduced, and the algorithm can be more quickly converged.

[0036] (4) The differential evolution algorithm is adopted in the present application, as a kind of efficient parallel optimization algorithm, new individual is generated by random deviation disturbance, the search result is guided to approach global optimal solution, the phase modulation parameters of the optical phased array beam splitter can be quickly iterated and solved, and the present application has strong robustness and global optimization ability. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 It is the algorithm flow chart of the present application.

[0038] Figure 2 It is the real-time monitoring schematic diagram of far-field spot light intensity of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose and advantages of the present application more clear and obvious, the present application is described in detail below in combination with the drawings and examples.

[0040] The embodiment adopts mutation operation based on adaptive differential evolution and "one-to-one" competition survival strategy, uses floating-point number coding, and performs optimization calculation in continuous space, and an algorithm flow is as shown in Figure 1

[0041] (1) input control parameters of the differential evolution algorithm, including population number N P , mutation operator F, crossover operator CR, maximum evolution generation G max , and termination condition G≥G max ; and the termination condition is set as reaching the maximum evolution generation.

[0042] (2) randomly generate an initial population, and let individuals be X i,g (i=1, 2,..., N P ), and evolution generation k=1.

[0043] (3) evaluate the initial population, that is, calculate the sidelobe suppression ratio corresponding to each individual in the initial population.

[0044] (4) based on an adaptive mutation operator, perform mutation operation to obtain Y i,g+1 (i=1, 2,..., N P ).

[0045] (5) select an interference parameter vector, perform crossover operation to obtain Z i,g+1 (i=1, 2,..., N P ).

[0046] (6) judge the boundary condition, if the element value in the Z i,g+1 vector is greater than or equal to k(X max -X min ), turn to step (7), if less than k(X max -X min ), turn to step (8). X max is the upper limit of the boundary value, and X min is the lower limit of the boundary value.

[0047] (7) for the value exceeding the boundary, randomly generate a new individual meeting the boundary constraint to obtain a temporary population.

[0048] (8) for the value exceeding the boundary, perform boundary absorption processing, and let the element value exceeding the boundary value in the Z i,g+1 vector equal to the boundary value to obtain a temporary population.

[0049] (9) evaluate the temporary population, and calculate the sidelobe suppression ratio of each individual in the temporary population.

[0050]

[0051] ​(10) for the individual in the temporary population and the corresponding individual in the original population, the "one-to-one" selection operation is carried out, and X i,g The corresponding side lobe suppression ratio Φ(X i,g ) and Z i,g+1 The corresponding side lobe suppression ratio Φ(Z i,g+1 ), the individual with the larger corresponding side lobe suppression ratio is selected to appear in the next generation population, so as to obtain a new generation population.

[0052] (11) judge whether the termination condition is reached or the maximum evolution number is reached: if yes, the evolution is terminated and step (12) is turned to output the optimal individual at this time as a solution; otherwise, the evolution number k=k+1, and step (4) is turned.

[0053] (12) output the optimal individual and end.

[0054] In step (4), It is obtained based on the mutation vector of the differential evolution algorithm for each target vector X ig (i=1, 2, …, N P ). In the above formula, the randomly selected serial numbers r1, r2 and r3 are different from each other, and r1, r2 and r3 are also different from the serial number i of the target vector, and must satisfy N P ≥4; the adaptive mutation operator F∈[0, 2]; the adaptive mutation operator F=F0×2 τ , Where F0 represents the mutation operator, G m represents the maximum evolution number, and G represents the current evolution number; the adaptive mutation operator is 2F0 at the beginning, has a larger value, and is helpful to maintain the diversity of the population individuals and avoid prematureness in the early stage; with the progress of the algorithm, the mutation operator gradually decreases, and the mutation rate approaches F0 in the later stage, retains good information, avoids the destruction of the optimal solution, and increases the probability of the global optimal solution.

[0055] In step (5),

[0056]

[0057] Where randb(j) represents the jth estimate value of the random number generator between [0, 1]; rnbr(i)∈(1, 2, …, D) represents a randomly selected sequence, which is used to ensure that Z i,g+1 At least one parameter is obtained from Y i,g+1 ; CR represents the crossover operator, and its value range is [0, 1].

[0058] The optical phased array far-field spot light intensity real-time monitoring method of the application is as follows:

[0059] The adaptive differential evolution algorithm model outputs the current phase modulation parameter vector to a parameter vector issuing module, which synchronously issues the phase modulation parameter vector of each channel to each channel of the optical phased array antenna. The optical phased array output beam is projected on a fixed screen. An infrared camera captures the far-field spot image on the fixed screen and transmits it into a computer. The main lobe peak light intensity and the side lobe peak light intensity are identified. The side lobe suppression ratio corresponding to the parameter vector is calculated and input into the differential evolution algorithm model, as shown in Figure 2 . .

[0060] The above examples are only specific examples for further detailing the purposes, technical solutions and beneficial effects of the present application, and the present application is not limited thereto. Any modification, equivalent replacement, improvement, etc. made within the disclosed range of the present application is included in the protection scope of the present application.

Claims

1. An optical phased array sidelobe suppression method based on adaptive differential evolution algorithm, characterized in that, The method comprises the following steps: S1, initializing the model and setting a termination condition Initialize the population size, adaptive mutation operator, crossover operator, and boundary conditions to preset values, and use the optical phased array parameter vector as the first-generation population parameter vector X. i,1 (i = 1, 2, ..., N) P ), and set a termination condition, where N P Let X be the maximum size of the initial population. i,1 For individuals in the initial population, X i,1 The subscript indicates that the individual is the i-th individual in the first generation population, i∈[1,Np]. The default value of the boundary condition is the individual X in the population. i,1 The corresponding upper bound of boundary value X max and boundary value lower limit X min ; S2, performing a mutation operation For the current population individual, a mutation operation based on an adaptive mutation operator is performed to obtain a mutated population parameter vector Y i,g+1 (i = 1, 2, …, N P ); S3, performing a crossover operation Y = Y + a * (X - Y) (1) i,g+1 Y = Y + a * (X - Y) (1) P ) with other target vectors' parameters X i,g ) with other target vectors' parameters X P ) with other target vectors' parameters X i,g+1 ) with other target vectors' parameters X P ) with other target vectors' parameters X S4, boundary condition judgment It is judged whether the generated sub-individual exceeds the preset value of the boundary condition, and if not, the process jumps to S5. If the boundary condition is exceeded: if the difference between the boundary constraint is not more than k times, the value of the sub-individual is set to the adjacent boundary value; if the difference between the boundary constraint is more than k times, the value of the sub-individual is replaced by a randomly generated parameter vector in the feasible region; S5, performing a selection operation The newly generated sub-individual Z i,g+1 (i = 1, 2, …, N P ) and the last generation individual X i,g (i = 1, 2, …, N P ) are brought into the sidelobe suppression ratio function Φ(X) = I (X) / I ′ (X) , (i = 1, 2, 3, …, N P ), and the individual with the maximum sidelobe suppression ratio function value is selected as the next generation; wherein Φ(X) = I (X) / I ′ (X) is the function mapping relationship of the optical phased array phase modulation parameter vector and the sidelobe suppression ratio, X is the optical phased array phase modulation parameter vector, and the vector dimension corresponds to the number of optical phased array channels; I (X) is the main lobe peak light intensity, which is the maximum light intensity near the main lobe position; I ′ (X) is the sidelobe peak light intensity, which is the maximum light intensity near the main lobe position after removing the main lobe peak light intensity; S6, steps S2 to S5 are executed in a loop, and the process stops when the termination condition is reached, and the current optimal individual is output, which is the approximate solution of the optimal solution of the model at this time, so as to obtain a parameter vector that optimizes the sidelobe suppression effect of the optical phased array.

2. The optical phased array sidelobe suppression method based on adaptive differential evolution algorithm according to claim 1, characterized in that: In step S2, for each target vector X ig (i = 1, 2, …, N P ), the mutation vector based on the differential evolution algorithm is as follows: wherein the randomly selected serial numbers r1, r2, r3 are different from each other, and r1, r2, r3 are also different from the serial number i of the target vector, and must satisfy N P ≥ 4; the adaptive mutation operator F ∈ [0, 2]; the adaptive mutation operator F = F0×2 τ , wherein F0represents a mutation operator, G m represents the maximum evolution generation, and G represents the current evolution generation; the adaptive mutation operator is 2F0at the beginning, has a large value, and is helpful to maintain the diversity of the population individuals in the initial stage, and avoid prematureness. As the algorithm progresses, the mutation operator gradually decreases, and the mutation rate approaches F0 in the later stage.

3. The optical phased array sidelobe suppression method based on adaptive differential evolution algorithm according to claim 1, characterized in that: In step S3, where randb(j) represents the jth estimate of a random number generator between [0, 1]; rnbr(i) e (1, 2,..., D) represents a randomly selected sequence used to ensure Z i,g+1 at least Y i,g+1 is obtained; CR represents a crossover operator with a value range of [0, 1].

4. The optical phased array sidelobe suppression method based on adaptive differential evolution algorithm according to claim 1, characterized in that: In step S1, in the initialization process, it is assumed that all the initialized populations are in accordance with the uniform probability distribution; let the limit of the parameter variable be X jmin <X j <X jmax X ji,0 = rand [0, 1] · (X jmax - X jmin + X jmin (i = 1, 2,..., NN p ; j = 1, 2,..., d), where rand[0, 1] represents a uniform random number generated between [0, 1].

5. The optical phased array sidelobe suppression method based on adaptive differential evolution algorithm according to claim 1, characterized in that: The real-time monitoring method of the optical phased array far-field spot light intensity is: The adaptive differential evolution algorithm model outputs the current phase modulation parameter vector to the parameter vector distribution module, which synchronously distributes the phase modulation parameter vector of each channel to each channel of the optical phased array antenna. The optical phased array emits a light beam onto a fixed screen, an infrared camera captures the far-field spot image on the fixed screen and transmits it to a computer, identifies the main lobe peak intensity and the sidelobe peak intensity of the optical phased array, calculates the sidelobe suppression ratio corresponding to the parameter vector, and inputs the differential evolution algorithm model.

6. The optical phased array sidelobe suppression method based on adaptive differential evolution algorithm according to claim 1, characterized in that: The optical phased array sidelobe suppression ratio calculation method is: an infrared camera captures a far-field light spot image corresponding to a phase modulation parameter vector X, the point with the maximum light intensity in the light spot image is recorded as the main lobe position of the optical phased array, the maximum light intensity near the point is recorded as the main lobe peak light intensity, that is, I (X) After removing the main lobe peak light intensity, the maximum light intensity near the point is recorded as the sidelobe peak light intensity, that is, I ′ (X) The function mapping relationship between the optical phased array phase modulation parameter vector and the optical phased array sidelobe suppression ratio is Φ(X)=I (X) / I ′ (X) , wherein X is the optical phased array phase modulation channel parameter vector, and the vector dimension corresponds to the number of optical phased array channels.

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