A grating lobe suppression method based on reconfigurable lens unit directional pattern

Abstract

The application relates to the technical field of antenna array directional diagram, and relates to a grating lobe suppression method based on a reconfigurable lens unit directional diagram. best，G The optimal decision variable X of each lens unit is calculated based on a differential evolution algorithm according to lens array parameters to suppress grating lobes. The reconfigurable lens antenna array grating lobe suppression technology based on the differential evolution algorithm breaks the periodicity of the array layout equivalently by using the reconfigurable directional diagram of the lens antenna, so that the effect of suppressing grating lobes is achieved, and the generation of grating lobes is reduced in a large-angle scanning range of the large-interval lens array.

Description

Technical Field

[0001] This invention relates to the field of antenna array pattern technology, and more specifically to a grating lobe suppression method based on reconfigurable lens element pattern. Background Technology

[0002] Existing large-spacing grating lobe suppression methods can be divided into two categories: (1) Non-periodic array element layout to suppress grating lobes: using random subarray arrangement or unequal spacing of elements to break the periodicity of the array surface, thereby suppressing grating lobes; (2) Array efficient active element pattern synthesis: the zero point position of the synthesized element pattern corresponds to the grating lobe position of the array factor pattern, thereby canceling energy and suppressing grating lobes.

[0003] The principle of using aperiodic array element layout to suppress grating lobes is to disrupt the periodicity of each subarray in the array, thereby redistributing the energy of the grating lobes. However, changing the position of subarrays or elements makes it difficult to arrange the corresponding feed network and RF components, and increases the manufacturing difficulty, making it unsuitable for large-scale array fabrication.

[0004] The principle of efficient active element pattern synthesis is the pattern multiplication principle, that is, the efficient element pattern is multiplied by the array factor to reduce or suppress grating lobes. However, when the array size is large, especially when the spacing is also large, that is, when there are many grating lobe positions of the array factor, such as reaching 3λ, and the scanning angle is also large, such as 45° scanning, it is difficult to satisfy the correspondence between multiple zeros and grating lobe positions by synthesizing the active element pattern.

[0005] Because lens antennas have a large aperture, a large array spacing is obtained when arranging them, which is much larger than the grating lobe suppression requirements for half-wavelength arrays. Under the condition of a large-spacing array layout, high grating lobes will inevitably be generated, thus affecting the radiation performance of the antenna. Reasonably reducing and avoiding grating lobes is a problem that must be considered when designing large-spacing arrays. Summary of the Invention

[0006] This invention provides a grating lobe suppression method based on a reconfigurable lens unit pattern, which overcomes the grating lobe problem under large-spacing layout conditions in the prior art.

[0007] This invention is achieved through the following technical solution:

[0008] A grating lobe suppression method based on reconfigurable lens unit radiation patterns calculates the optimal decision variable X for each lens unit using a differential evolution algorithm based on the lens array parameters. best，G To suppress the grid lobes.

[0009] As an optimization, the lens array parameters include the operating frequency of the lens units, the size of the lens array, the topology of the lens array, the selectable orientation pattern of the lens units, and the target scanning position.

[0010] As an optimization, the differential evolution algorithm calculates the optimal decision variable X for each lens unit. best，G The specific steps are as follows:

[0011] Step 1: Initialize the decision variables of the lens array, where the decision variable of the lens array is X. i，G ;

[0012] Step 2: Initialize the decision variable X i，G Substitute the evaluation function Fit into the value and perform the calculation. If the evaluation function Fit is greater than the set threshold, proceed to step 3; otherwise, the decision variable is the optimal decision variable X. best，G Output the optimal decision variable X best，G The value;

[0013] Step 3: Perform iterative operations of difference, crossover, and selection on the decision variables to calculate new decision variables, and return to step 2.

[0014] As an optimization, the specific calculation method for performing the difference operation on the decision variables in step 3 is as follows:

[0015]

[0016] Among them, X best，G represents the individual with the optimal fitness value in the G-th iteration (i.e., the optimal decision variable obtained), F is a positive parameter in the range [0,1], called the scaling factor, which is used to control the degree of differential perturbation, and i represents the position of the lens unit.

[0017] As an optimization, the specific calculation method for performing cross operations on the decision variables in step 3 is as follows:

[0018]

[0019] Among them, rand j It is a randomly generated integer in the range [1,D], where D is the vector dimension and CR is the crossover factor in the range [0,1]. Like F, it is a parameter that controls the differential evolution algorithm.

[0020] As an optimization, the specific calculation method for selecting the decision variables in step 3 is as follows:

[0021]

[0022] As an optimization, the optimal decision variable X best，G Equivalent scanning pattern angle including the reconfigurable pattern distribution of the lens unit And the feed phase α.

[0023] As an optimization, the expression for the evaluation function Fit is: Fit = PSLL - kG + C, where k is a positive weighting coefficient, C is a constant, PSLL is the grating lobe value of the lens unit, and G is the gain of the lens unit.

[0024] As an optimization, the expression for the grating lobe value is: Where q is the position of the q-th lens unit, θ∈[0, θ] max ], α∈[0, 360°].

[0025] As an optimization, the gain G is G in, The target scanning location.

[0026] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0027] 1. This invention is based on the differential evolution algorithm for reconfigurable lens antenna array grating lobe suppression technology. It utilizes the reconfigurable radiation pattern of the lens antenna to effectively break the periodicity of the array layout, thereby achieving the effect of suppressing grating lobes and ensuring that the generation of grating lobes is reduced in the large-spacing lens array within a large-angle scanning range.

[0028] 2. This invention does not change the position of the array elements, which facilitates the large-scale fabrication of the array and the design of the power supply network. At the same time, the grating lobe suppression effect is better. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0030] Figure 1 This is a flowchart of the algorithm for a grating lobe suppression method based on a reconfigurable lens unit pattern as described in this invention.

[0031] Figure 2 This is a top view schematic diagram of the linear arrangement of the lens units in the embodiment;

[0032] Figure 3 This is a side view schematic diagram of the linear arrangement of the lens units in the embodiment;

[0033] Figure 4 This is the normal direction pattern of the lens array in the embodiment;

[0034] Figure 5 The image shows a 40° scanning pattern of the lens array in this embodiment (with grating lobes).

[0035] Figure 6 The image shows a 40° scanning pattern of the lens array in this embodiment (without grating lobes).

[0036] Figure 7 The image shows the lens array at a 45° angle (without grating lobes) in this embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0038] Example

[0039] This invention discloses a grating lobe suppression method based on the radiation pattern of a reconfigurable lens unit. The optimal decision variable X for each lens unit is calculated using a differential evolution algorithm based on the lens array parameters. best，G To suppress the grid lobes.

[0040] For a lens array, its radiation pattern can be represented as:

[0041]

[0042] Where E (p) Let Q be the p-th reconfigurable radiation pattern of the lens element (antenna). Different reconfigurable radiation patterns are selected for the lens antenna to suppress grating lobes aperiodically. The ultimate goal of optimization is to find a distribution of reconfigurable radiation patterns for the lens antenna, i.e.:

[0043] The equivalent optimization objective is to find a set of scan pattern angles for a subarray, namely:

[0044]

[0045] The novel grating lobe algorithm provided by this invention includes the following steps, and the algorithm flow is as follows: Figure 1 As shown.

[0046] Step A: Input lens array parameters; Step B: Based on differential evolution algorithm.

[0047] Specifically:

[0048] In step A, the lens array parameters include the operating frequency, the size and topology of the lens array, the selectable radiation pattern of the lens unit, i.e., the maximum scanning range of the lens unit, and the target scanning position.

[0049] In step B, the selection of the reconfigurable lens unit pattern and the array feed phase are optimized based on the differential evolution algorithm.

[0050] Step B also includes the following steps:

[0051] Step B1----Step B2----Step B3----Step B4----Step B5.

[0052] Step B1 initializes the lens array population and selects the equivalent scanning pattern angle of the reconstructable pattern distribution of the lens units. Initialize the feed phase ∝ using the feed phase α as the initial variable. q :

[0053]

[0054] in x represents the target scanning position to be scanned in the radiation pattern. q and y q These are the x and y coordinates of the position of the lens unit, respectively.

[0055] Step B2 involves selecting the evaluation function, with the objective of grating lobe suppression. The optimization problem can be written as follows:

[0056]

[0057] Then, the evaluation function is expressed in terms of grating lobe value and gain as follows:

[0058] Fit = PSLL - kG + C;

[0059] Here, k is a positive weight coefficient, and C is a constant to ensure Fit > 0. This avoids the iteration termination in subsequent steps B3-B5, which would prevent the optimal decision variable X from being found. best，G .

[0060] When the evaluation function Fit satisfies the threshold, PSLL can be considered as the minimum value of the grating lobe, while the gain G G is the gain value at the target scanning position. When the target scanning position is reached, G... The value is the largest.

[0061] Specific examples Figure 2-3 As shown, in step A, input the lens array parameters, such as... Figure 2 As shown, eight lens units are arranged linearly with a spacing of 3.8λ between units. The maximum scanning range of the lens units is (θ). max ±40°, target scanning positions (45°, 0°) and (40°, 0°).

[0062] In step B, the selection of the reconfigurable lens unit pattern and the array feed phase are optimized based on the differential evolution algorithm.

[0063] Step B also includes the following steps:

[0064] Step B1----Step B2----Step B3----Step B4----Step B5.

[0065] Step B1 initializes the population and calculates the evaluation function for the optimization problem, with the decision variable being the optimization objective X. i,G It can be represented as

[0066]

[0067] In the formula, i represents the i-th individual in the population, D represents the vector dimension, where D = 3Q, Q is the number of lens units, the population size is set to NP, and the maximum number of iterations is G. max Selecting a lens element allows for the reconstruction of the equivalent scanning pattern angle of the pattern distribution. Initialize the feed phase α as an initial variable. q The decision variables in the search space are constrained by the objective problem and can be represented as a lower bound x. min =-θ max Upper boundary x max =θ max The initial population can then be represented as

[0068]

[0069] Here, rand(0,1) represents a uniformly distributed random number in the interval [0,1], and j is the vector dimension.

[0070] In step B2, the parameters of the evaluation function are selected as k = 0.2 and C = 20.

[0071] Fit = PSLL - kG + C;

[0072] Step B3 performs the differential mutation operation:

[0073]

[0074] X best，G It is the individual with the optimal fitness value in the G-th iteration. F is a positive parameter, ranging from [0,1], called the scaling factor, which is used to control the degree of differential perturbation.

[0075] Step B4 involves a crossover operation, specifically a binomial crossover operation, to enhance population diversity. This invention employs a binomial crossover operation to obtain new individuals.

[0076]

[0077] rand jIt is a randomly generated integer in the range [1,D]. CR is the crossover factor, which is in the range [0,1]. Like F, it is a parameter that controls the DE algorithm.

[0078] Step B5 involves a selection operation, greedily choosing the individual with better fitness from the parent and experimental individuals to serve as the parent of the next generation.

[0079]

[0080] The above three steps—differentiation, mutation, and selection—are repeated generation after generation until the conditions are met.

[0081] Simulation results:

[0082] For a linear array of 8×1 lens elements arranged in a 3.8λ configuration, the normal radiation pattern is as follows when θmax = 40°: Figure 4 As shown, the sidelobe level is -11.55dB. When scanning at 40°, severe grid lobes appear, such as... Figure 5 As shown, the reconfigurable radiation pattern of the lens unit is selected using the differential evolution algorithm, and the result is as follows. Figure 6 As shown, the grating lobe is suppressed to -11.62 dB, demonstrating a significant grating lobe suppression effect. Further scanning to 45°, the reconfigurable radiation pattern of the lens unit is selected using a differential evolution algorithm, yielding the following result: Figure 7 As shown, the grid lobe is suppressed to -8.77dB, and the grid lobe suppression effect is slightly reduced, but still significant.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A grating lobe suppression method based on a reconfigurable lens unit pattern, characterized in that, The optimal decision variables for each lens unit are calculated based on the lens array parameters using a differential evolution algorithm. To suppress grating lobes; the optimal decision variable Equivalent scanning pattern angle including the reconfigurable pattern distribution of the lens unit and feed phase This achieves the effect of suppressing grating lobes, ensuring that the generation of grating lobes is reduced within a large-angle scanning range by the large-pitch lens array.

2. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 1, characterized in that, The lens array parameters include the operating frequency of the lens units, the size of the lens array, the topology of the lens array, the selectable orientation pattern of the lens units, and the target scanning position.

3. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 2, characterized in that, The differential evolution algorithm calculates the optimal decision variables for each lens unit. The specific steps are as follows: Step 1: Initialize the decision variables of the lens array. The decision variables of the lens array are... ; Step 2: Initialize the decision variables Substitute the evaluation function Fit into the value and perform the calculation. If the evaluation function Fit is greater than the set threshold, proceed to step 3; otherwise, the decision variable is the optimal decision variable. Output the optimal decision variables The value; Step 3: Perform iterative operations of difference, crossover, and selection on the decision variables to calculate new decision variables, and return to step 2.

4. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 3, characterized in that, In step 3, the specific calculation method for performing the difference operation on the decision variables is as follows: ； in, represents the individual with the best fitness value in the G-th iteration, F is a positive parameter in the range [0,1], called the scaling factor, which is used to control the degree of differential perturbation, and i represents the position of the lens unit.

5. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 4, characterized in that, In step 3, the specific calculation method for performing cross operations on the decision variables is as follows: ； in, It is a randomly generated integer in the range [1,D], where D is the vector dimension and CR is the cross factor in the range [0,1].

6. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 5, characterized in that, In step 3, the specific calculation method for performing the selection operation on the decision variables is as follows: 。 7. A grating lobe suppression method based on a reconfigurable lens unit pattern according to any one of claims 1-6, characterized in that, The expression for the evaluation function Fit is: Where k is a positive weighting coefficient and C is a constant. is the grating lobe value of the lens unit, and Gain is the gain of the lens unit.

8. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 7, characterized in that, The expression for the grating lobe value is: Where q is the position of the q-th lens unit. .

9. The grating lobe suppression method based on a reconfigurable lens unit pattern according to claim 7, characterized in that, The gain Gain is Gain ( ),in,( () is the target scanning location.

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