Approximate flat-top beam shaping method based on PSO optimization algorithm

Abstract

The application discloses a kind of based on PSO optimization algorithm's approximate flat-top wave shaping method, comprising the following steps: S1: the basic field intensity directional diagram function at the far field of transmitting antenna array is constructed;S2: constructing approximate flat-top main beam field intensity directional diagram function;S3: the function is brought into PSO optimization algorithm, and approximate flat-top beam shaping operation is carried out.Compared with prior art, the application uses PSO optimization algorithm to optimize calculation on transmitting antenna array input signal parameter, avoids complex formula derivation and simplification, convergence speed is faster than other algorithms, the optimal parameter obtained by using PSO optimization algorithm is used to adjust transmitting antenna array input signal parameter, it can be seen that the waveform of transmitting signal of transmitting antenna array falling on receiving antenna array is approximately flat-top wave;The application inserts a phase in approximate flat-top main beam field intensity directional diagram function, promotes main beam waveform to be more close to flat-top wave, makes main lobe direction gain increase, so that transmission efficiency rises.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antennas, and particularly relates to a method for shaping an approximately flat top wave based on a PSO optimization algorithm. BACKGROUND

[0002] Microwave wireless energy transmission system (MWPT) has wide application prospects as one of wireless energy transmission technologies, such as wireless energy transmission for unmanned aerial vehicles, sensor networks, solar power stations, etc. The microwave wireless energy transmission system includes a transmitting antenna system and a receiving antenna system. The transmitting antenna system is used to focus and control the radiated high-power microwave beam, and the receiving antenna is used to receive the irradiated high-power microwave energy, which is then converted into direct current by the rectifier circuit at the back end and transmitted to the back end.

[0003] In order to receive as much energy as possible by the receiving antenna system, the antenna aperture at the receiving end is generally large. When the gain of the transmitting antenna system is very high, the power density distribution at the receiving end is no longer uniformly distributed but is taperedly decreasing. When the rectifying antenna is distributed in an array for distributed rectification, the efficiency is reduced. Therefore, in order to improve the receiving efficiency, the antenna needs to be shaped for an approximately flat top beam.

[0004] Currently, the main problems of flat top beam shaping are that the main lobe ripple is unstable and the difference is large, and the maximum gain of the side lobe is too high. These problems affect the power density distribution at the receiving end, and further affect the receiving efficiency of the receiving antenna array. SUMMARY

[0005] The present application discloses a method for shaping an approximately flat top wave based on a PSO optimization algorithm, which inserts a phase in the approximately flat top main beam field intensity pattern function to make the main beam waveform closer to the flat top wave and increase the main lobe directivity gain, thereby increasing the transmission efficiency. The PSO optimization algorithm is used to optimize the calculation of the input signal parameters of the transmitting antenna array, avoiding complex formula derivation and simplification, and the convergence speed is faster than other algorithms.

[0006] TECHNICAL SCHEME The present application discloses a method for shaping an approximately flat top wave based on a PSO optimization algorithm, comprising the following steps:

[0007] S1: constructing a basic field intensity pattern function at the far field of the transmitting antenna array:

[0008]

[0009] Wherein, n is the number of antennas, Ψ is the phase deviation between antennas, d is the antenna spacing, λ is the wavelength, k is the wave number, θ is [-179.5 ° , 180 °random number; λ = c / f, c is the speed of light, f is the frequency; N is the number of antenna array elements, j is the imaginary part;

[0010] S2: According to the basic field strength pattern function in S1, an approximate flat main beam field strength pattern function is constructed:

[0011]

[0012] Wherein: S is the transmitting antenna array transmitting signal, 1 / N is energy normalization, γ n is a random number (0°, 360°);

[0013] S3: The function in S2 is brought into the PSO optimization algorithm, and the approximate flat beam shaping operation is performed.

[0014] Further, the specific steps of constructing the basic field strength pattern function at the far field of the transmitting antenna array in S1 are:

[0015] S1.1: Construct an antenna array of n elements;

[0016] S1.2: The distance formula and time formula for the signal of one element to travel more than the previous element are:

[0017] Δ d = (n-1)dsinθ; Δ t = (n-1)dsinθ / c;

[0018] S1.3: The phase deviation of the signal of one element compared with the previous element is Ψ under single frequency condition;

[0019] S1.4: The signal at the far field transmitted by n elements together is E θ , which is the basic field strength pattern function at the far field of the transmitting antenna array.

[0020] Further, the specific steps of constructing the approximate flat main beam field strength pattern function in S2 are:

[0021] S2.1: The basic field strength pattern at the far field of the transmitting antenna array in step S1 is used, and the excitation of the transmitting antenna array is S at this time;

[0022] S2.2: At this time, the signal received by the receiving antenna array is determined as E plot (θ).

[0023] Further, the specific steps of using the PSO optimization algorithm for approximate flat beam shaping operation in S3 are:

[0024] S3.1: Construct a PSO algorithm model and initialize the population;

[0025] S3.2 Constructing the fitness function of PSO algorithm, which is the approximate flat main beam field intensity pattern function E in S2 plot (θ), and calculating the fitness of each particle;

[0026] S3.3 Updating the historical best fitness of population, and disturbing the best fitness by using the disturbing formula;

[0027] S3.4 Updating the velocity formula and position formula of particle swarm according to the best fitness, and optimizing them;

[0028] S3.5 Determining whether the PSO algorithm has reached the maximum iteration number, if yes, outputting; if no, continuing to run until reaching the maximum iteration number.

[0029] Further, the disturbing formula in S3.3 is:

[0030] w=0.9-0.7*(ger-g) / ger (4)

[0031] wherein w is the weight value, ger is the maximum iteration number, g is the iteration number,

[0032]

[0033] wherein c1 is the individual self-cognition learning factor, and c2 is the social cognition learning factor.

[0034] Further, the velocity updating formula and position updating formula in S3.4 are:

[0035]

[0036] θ(t+1)=θ(t)+v (8)

[0037] wherein v(t+1) is the velocity updating formula, θ(t+1) is the position updating formula, θ best is the historical best fitness, is the global best fitness, and rand is a random number between 0 and 1.

[0038] Further, the formula in S2 is taken as the fitness function of PSO algorithm, and the approximate flat beam pattern and convergence graph are generated by using MATLAB software.

[0039] Beneficial effects:

[0040] 1、The application applies PSO algorithm to approximate flat-top wave shaping, uses PSO optimization algorithm to optimize and calculate the input signal parameters of the transmitting antenna array, obtains the input signal of the transmitting antenna array, and promotes the waveform of the transmitting signal falling on the receiving antenna array to be approximate flat-top wave; avoids the derivation and simplification of a large number of complex formulas, reduces the types of rectifier circuits on the receiving antenna array, and reduces the manufacturing cost and difficulty of the receiving antenna array. In the case that the PSO algorithm is easy to fall into local optimum, disturbance is added at the local optimum to promote the PSO algorithm to achieve global optimum. The PSO optimization algorithm is used in the application field, and the convergence speed is faster than other intelligent algorithms.

[0041] 2、The application inserts a phase in the approximate flat-top main beam field intensity pattern function, promotes the main beam waveform to be closer to flat-top wave, increases the main lobe direction gain, and thus increases the transmission efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is an approximate flat-top beam and ideal flat-top beam pattern based on the PSO optimization algorithm according to the application;

[0043] Figure 2 is a schematic diagram of the transmitting antenna array and the receiving antenna array according to the application;

[0044] Figure 3 is a flowchart of an approximate flat-top wave shaping method based on the PSO optimization algorithm according to the application;

[0045] Figure 4 is a convergence diagram of an approximate flat-top wave shaping method based on the PSO optimization algorithm according to the application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the application will be clearly and completely described in connection with the drawings in the embodiments of the application. The described embodiments are only some of the embodiments of the application, not all the embodiments. Without departing from the design concept of the application, various modifications and improvements to the technical solutions of the application made by those skilled in the art shall fall within the protection scope of the application.

[0047] The application discloses an approximate flat-top wave shaping method based on a PSO optimization algorithm, as shown in the approximate flat-top beam pattern based on the PSO optimization algorithm, mainly comprising the following steps: Figure 1

[0048] S1: constructing a basic field intensity pattern function at the far field of the transmitting antenna array:

[0049]

[0050] ​Where n is the number of antennas and Ψ is the phase deviation between antennas.

[0051]

[0052] Where: d is the antenna spacing, λ is the wavelength, k is the wave number, and θ ranges from [-179.5° to 180°]. λ = c / f, where c is the speed of light and f is the frequency. N is the number of antenna elements, and j is the imaginary part.

[0053] S2: Construct an approximate flat-top main beam field strength pattern function:

[0054]

[0055] Where: S is the transmitting antenna array transmission signal, 1 / N is the energy normalization, γ n A random number between (0°, 360°).

[0056] S3: Bring the function of S2 into the PSO optimization algorithm to perform an approximate flat-top beamforming operation.

[0057] like Figure 2 As shown in the figure, the schematic diagram of the transmitting antenna array and the receiving antenna array is mainly implemented by the following steps:

[0058] S1.1 Construct an antenna array with n elements.

[0059] S1.2 The formula for the distance and time that a signal from one element travels more than that from the previous element is: d =(n-1)dsinθ; Δ t =(n-1)dsinθ / c.

[0060] S1.3 In the single-frequency case, the phase deviation of the signal of one array element compared to the previous array element is Ψ.

[0061] S1.4 The far-field signal emitted by n array elements is E θ .

[0062] like Figure 3 、 Figure 4 The flowchart and convergence diagram of an approximate flat-top wave shaping method based on the PSO optimization algorithm are shown, which is mainly implemented by the following steps:

[0063] S3.1 Build the PSO algorithm model and initialize the population.

[0064] S3.2 constructs the fitness function of the PSO algorithm. The fitness function of the PSO algorithm is the approximate flat-top main beam field intensity pattern function in S2, and calculates the fitness of each particle.

[0065] S3.3 update the population history optimum fitness, and disturb the optimum fitness.

[0066] The disturbing formula of step S3.3 is:

[0067] w=0.9-0.7*(ger-g) / ger (4)

[0068] Wherein: w is weight value, ger is maximum iteration number, and g is iteration number.

[0069]

[0070] Wherein: c1 is individual self-cognition learning factor, and c2 is social cognition learning factor.

[0071] S3.4 update particle swarm speed formula and position formula according to optimum fitness, and optimize them.

[0072] The speed update formula and position update formula of step S3.4 are:

[0073]

[0074] θ(t+1)=θ(t)+v (8)

[0075] Wherein: v(t+1) is speed update formula, θ(t+1) is position update formula, θ best is history optimum fitness, is global optimum fitness, and rand is random number between (0, 1).

[0076] S3.5 determine whether the current PSO algorithm has reached maximum iteration number or not, output if yes, and continue running until maximum iteration number is reached if no.

[0077] The approximate flat-top main beam field intensity directional diagram function is brought into the PSO algorithm, and the approximate flat-top beam directional diagram and convergence diagram are generated by using MATLAB software.

[0078] The advantages of the present application can be further illustrated by the following numerical simulation experiment:

[0079] 1. Simulation parameters

[0080] Both the transmitting antenna and the receiving antenna are array antennas, the working frequency is 915MHz, the input signal of the transmitting antenna array has both amplitude and phase, the amplitude is fixed, the phase is indefinite, and the phase range is (0 ° , 360 ° ).

[0081] 2. Simulation content and result

[0082] In the present invention, a fitness function is provided as an approximate flat-top main beam field strength pattern function, equation (3) is introduced into the PSO optimization algorithm, the amplitude of the input signal collected from the antenna array is imported into the PSO optimization algorithm, the phase of the input signal of the antenna array is set as the target value, and brought into the PSO optimization algorithm. After MATLAB simulation, the following data are obtained, as shown in Table 1.

[0083] Table 1 Main / side lobe gains

[0084] Main lobe maximum gain 0 DB Main lobe minimum gain -0.9545 DB Side lobe maximum gain -13.11 DB

[0085] From the data in the above table, it can be clearly seen that the ripple fluctuation of the main beam flat-top wave obtained by the approximate flat-top wave shaping method based on the PSO optimization algorithm of the present invention does not exceed 1DB, and the sidelobe beam gain is lower than -12DB, which truly achieves high main lobe gain and uniform energy distribution, and low sidelobe gain, thereby greatly improving the receiving efficiency of the receiving antenna array.

[0086] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made in accordance with the spirit of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. An approximate flat-top wave shaping method based on PSO optimization algorithm, characterized in that: The following steps are involved: S1: Construct the basic field strength pattern function in the far field of the transmitting antenna array: Where: n is the number of antennas, Ψ is the phase deviation between antennas; d is the antenna spacing, λ is the wavelength, k is the wave number, and θ ranges from [-179.5° to 180°]; λ = c / f, where c is the speed of light and f is the frequency; N is the number of elements in the antenna array, and j is the imaginary part. S2: Based on the basic field strength pattern function obtained in S1, construct an approximate flat-top main beam field strength pattern function: Where: S is the transmitting antenna array transmission signal, 1 / N is the energy normalization, γ n A random number for (0°, 360°); S3: Bring the function in S2 into the PSO optimization algorithm to perform an approximate flat-top beamforming operation; S3.1 build the PSO algorithm model and initialize the population; S3.2 constructs the PSO algorithm fitness function, which is the approximate flat-top main beam field intensity pattern function E in S2. plot (θ), and calculate the fitness of each particle; S3.3 Update the historical best fitness of the population and perturb the best fitness using the perturbation formula; The perturbation formula is: w=0.9-0.7*(ger-g) / ger (4) Among them: w is the weight, ger is the maximum number of iterations, g is the number of iterations, Among them: c1 is the individual self-cognition learning factor; c2 is the social cognition learning factor; S3.4 updates the particle swarm velocity formula and position formula according to the best fitness and optimizes them; The velocity update formula and position update formula are: θ(t+1)=θ(t)+v (8) Among them: v(t+1) is the velocity update formula, θ(t+1) is the position update formula, θ best is the historical optimal fitness, is the global optimal fitness, rand is a random number between (0, 1); S3.5 determines whether the current PSO algorithm has reached the maximum number of iterations. If so, it outputs the result; otherwise, it goes to S3.2 and continues to run until the maximum number of iterations is reached.

2. The method for shaping approximate flat-top waves based on the PSO optimization algorithm according to claim 1, characterized in that: The specific steps of constructing the basic field strength pattern function at the far field of the transmitting antenna array in S1 are as follows: S1.1: Construct an antenna array with n elements; S1.2: The formula for the distance and time that a signal from one element travels longer than that from the previous element is: D d =(n-1)d sinθ;Δ t =(n-1)d sinθ / c; S1.3: In the case of single frequency, the phase deviation of the signal of one array element from the previous array element is Ψ; S1.4: The far-field signal emitted by n array elements is E θ , which is the basic field strength pattern function in the far field of the transmitting antenna array.

3. The method for shaping approximate flat-top waves based on the PSO optimization algorithm according to claim 1 is characterized in that: The specific steps of constructing the approximately flat-top main beam field strength pattern function in S2 are: S2.1: Based on the basic field strength pattern at the far field of the transmitting antenna array in step S1, the excitation of the transmitting antenna array at this time is S; S2.2: At this time, it is determined that the signal received by the receiving antenna array is E plot (θ).

4. The method for shaping an approximate flat-top wave based on a PSO optimization algorithm according to any one of claims 1 to 3, characterized in that: The formula in S2 is used as the fitness function of the PSO algorithm, and the approximate flat-top beam pattern and convergence diagram are generated using MATLAB software.

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