A low-sidelobe pattern synthesis method, device, equipment and storage medium

Abstract

The application discloses a low sidelobe pattern synthesis method, device and equipment and a storage medium, relates to the technical field of antennas, and comprises the following steps: simplifying an array factor response expression according to the structural symmetry of an antenna array to be optimized and representing the array factor response expression in a vector form; taking the maximum simplified array factor response on discrete sampling points in a sidelobe region as an optimization target, taking the initial upper limit and lower limit of the main lobe gain and the element weight value of the antenna array to be optimized as constraints, and modeling the low sidelobe pattern synthesis problem as a linear programming problem; solving the linear programming problem to obtain optimized element weight values and corresponding array factor patterns; if the system EIRP or G / T value does not satisfy the corresponding set value, adjusting the initial upper limit and lower limit of the element weight value, and solving the linear programming problem again. The application reduces the optimization variable dimension and the constraint number of the convex optimization problem, and greatly improves the operation efficiency under the condition of global optimization.

Description

Technical Field

[0001] This invention relates to the field of antenna technology, and in particular to a low sidelobe pattern synthesis method, apparatus, device, and storage medium. Background Technology

[0002] Antenna arrays utilize the property of coherent superposition of electromagnetic waves, and compared with a single antenna, they can achieve higher gain, stronger directivity and narrower beam. Therefore, they are widely used in wireless communication, radar detection, satellite navigation and radio astronomy, and play a key role in modern information systems.

[0003] Traditional arrays employ uniform feeding. For a large-scale array with a rectangular grid, the sidelobe height is approximately -13.5 dB. To ensure effective operation in complex environments, low-sidelobe pattern synthesis methods need to be studied to reduce peak sidelobes, improve the system's signal-to-interference-plus-noise ratio (SNR), thereby enhancing the array's anti-interference capability, improving overall system performance, and meeting the evolving needs of information systems. Low-sidelobe pattern synthesis, in the field of array signal processing, refers to optimizing the radiation pattern of an antenna array using specific techniques and methods to reduce the intensity of radiated beams outside the desired radiation direction. In practical engineering applications, the dynamic range of analog phased array attenuators / amplifiers is limited, and the link design of the array system must consider indicators such as EIRP (Equivalent Isotropic Radiated Power) and G / T values, where G is the antenna gain and T is the system noise. Therefore, low-sidelobe pattern synthesis algorithms need to consider the dynamic range of element excitation simultaneously. Under the requirements of the link budget, element weights can be adjusted to improve system robustness and meet system design specifications.

[0004] For antenna arrays with rectangular grid structures, the element weights can be designed using window function methods from traditional digital filter design, such as Taylor synthesis and Chebyshev synthesis, to reduce peak sidelobes. However, these window function methods cannot control the dynamic range of element excitation, and for general arrays with other grid structures, sampling or interpolation of the window function method results is often required, making them unsuitable for general grid arrays and unable to guarantee a globally optimal synthesis result. Intelligent optimization methods such as genetic algorithms and particle swarm optimization can also be used for low sidelobe pattern synthesis problems; however, when the number of elements is large, the dimensionality and degrees of freedom of the optimization variables are high, resulting in slow optimization speed and low computational efficiency. Furthermore, intelligent optimization methods also cannot guarantee a globally optimal solution. Numerical optimization methods based on convex optimization can guarantee a globally optimal solution, but the challenge lies in how to theoretically model the low sidelobe pattern synthesis problem of arrays while considering the constraints of the element dynamic range. Different optimization models have different computational complexities and optimization objectives. The low sidelobe pattern synthesis method based on second-order cone programming aims to minimize the sidelobe level and can find the optimal solution that minimizes the sidelobes in the visible area. However, it has a large computational load and the problem modeling cannot take into account the aperture efficiency and indicators such as EIRP and G / T value.

[0005] In summary, existing low sidelobe pattern synthesis algorithms have limited ability to control the dynamic range of array elements, array EIRP, and G / T value in array system design, and cannot balance computational efficiency and solution accuracy. There is an urgent need for a low sidelobe pattern synthesis method that is efficient, has controllable indicators, and yields optimal results. Summary of the Invention

[0006] Based on the shortcomings of the existing technology, the present invention provides a low sidelobe pattern synthesis method, apparatus, device and storage medium, which solves the problem that the existing low sidelobe pattern synthesis algorithms have limited ability to control the array system design parameters such as the dynamic range of array elements, array EIRP and G / T value, and cannot balance computational efficiency and solution accuracy.

[0007] The present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a low sidelobe pattern synthesis method, comprising the following steps:

[0009] Set the initial upper and lower bounds of the array element weights based on the dynamic range of the RF link of the antenna array to be optimized;

[0010] The structural symmetry of the antenna array to be optimized is obtained. Based on the structural symmetry, the array factor response expression is simplified and represented in vector form to obtain the simplified array factor response expression.

[0011] The low sidelobe pattern synthesis problem is modeled as a linear programming problem, with the optimization objective being to minimize the maximum simplified array factor response at discrete sampling points in the sidelobe region and the constraints being to maintain the initial upper and lower bounds of the main lobe gain and array element weights of the antenna array to be optimized.

[0012] Solve the linear programming problem to obtain the optimized element weights and corresponding array factor radiation patterns. Obtain the equivalent isotropic radiated power (EIRP) or G / T value of the antenna array system based on the optimized element weights. If the EIRP or G / T value does not meet the corresponding set value, adjust the initial upper and lower bounds of the element weights and solve the linear programming problem again. If it meets the set value, output the optimized element weights and corresponding array factor radiation patterns.

[0013] Preferably, the step of obtaining the structural symmetry of the antenna array to be optimized, simplifying the array factor response expression based on the structural symmetry and representing it in vector form to obtain the simplified array factor response expression specifically includes the following steps:

[0014] For an array with 2M+1 cells and a centrally symmetric layout, under amplitude-weighted optimization, its angle... The array factor pattern expression is shown below:

[0015]

[0016] in,

[0017]

[0018] In the formula, Let k be the array factor pattern expression for a centrosymmetric array. x and k y There are two wavenumber components, where λ is the wavelength corresponding to the array's operating frequency, (x m ,y m ) represents the position of the m-th array element, w0 is the excitation coefficient of the central array element, and θ is the polar angle. For azimuth, w m Let be the excitation coefficient of the m-th array element;

[0019] In a centrosymmetric array, the sidelobe region to be optimized is θ sl The initial polar angle of the sidelobe region to be optimized;

[0020] For an array with 4M+1 cells and a mirror-symmetric layout, under amplitude-weighted optimization, its angle... The array factor pattern expression is shown below:

[0021]

[0022] In a mirror-symmetric array, the sidelobe region to be optimized is:

[0023] The simplified matrix factors are represented in vector form:

[0024]

[0025] In the formula, To simplify the matrix factor response expression, h n Let w be the guiding vector in the nth direction, and w be the simplified array element weight vector.

[0026] Preferably, the low sidelobe pattern synthesis problem is modeled as a linear programming problem, as detailed below:

[0027]

[0028] α0≤w m ≤β0,m=0,···,M,

[0029]

[0030] In the formula, Let g be an M+1 dimensional vector composed of positive real numbers, g be the maximum reduced matrix factor response of the discretized sidelobe region to be optimized, and N be the number of sampling points in the sidelobe region. The main lobe response is defined by α0 as the initial upper bound of the element weights and β0 as the initial lower bound of the element weights.

[0031] Preferably, the output of the optimized array element weights and corresponding array factor pattern includes the following steps:

[0032] Obtain the guide vectors in different directions;

[0033] By inputting the steering vectors in different directions and the optimized array element weights into the simplified array factor response expression, array factor pattern at different angles can be obtained.

[0034] In a second aspect, the present invention provides a low sidelobe pattern synthesizer, comprising:

[0035] The setting module is used to set the initial upper and lower bounds of the array element weights based on the dynamic range of the RF link of the antenna array to be optimized.

[0036] The simplification module is used to obtain the structural symmetry of the antenna array to be optimized. Based on the structural symmetry, the array factor response expression is simplified and represented in vector form to obtain the simplified array factor response expression.

[0037] The module is constructed with the optimization objective of minimizing the maximum simplified array factor response at discrete sampling points in the sidelobe region, and with the constraints of maintaining the initial upper and lower bounds of the main lobe gain and array element weights of the antenna array to be optimized, and the low sidelobe pattern synthesis problem is modeled as a linear programming problem.

[0038] The solution module is used to solve the linear programming problem, obtain the optimized array element weights and the corresponding array factor radiation pattern, and obtain the equivalent isotropic radiated power (EIRP) or G / T value of the antenna array system based on the optimized array element weights. If the EIRP or G / T value does not meet the corresponding set value, the initial upper and lower bounds of the array element weights are adjusted, and the linear programming problem is solved again; if it meets the set value, the optimized array element weights and the corresponding array factor radiation pattern are output.

[0039] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned low sidelobe pattern synthesis method.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned low sidelobe pattern synthesis method.

[0041] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0042] This invention utilizes the structural symmetry of the antenna array layout to simplify the representation of the array factor response. A linear programming problem is constructed with the optimization objective of minimizing the maximum simplified array factor response at discrete sampling points within the sidelobe region. This reduces the dimensionality of the optimization objective and the number of constraints, significantly improving computational efficiency while maintaining solution accuracy. The linear programming problem also considers the dynamic range constraints of the array elements, and the corresponding hardware specifications are obtained based on the solution results. This invention allows the optimization results to be adjusted according to the system link budget and hardware specifications, thereby improving system robustness and reducing error sensitivity. It can be used for amplitude weighting of large-scale arrays, providing a powerful tool for the optimization design of applications such as spaceborne phased arrays. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1This is a flowchart of a low sidelobe pattern synthesis method according to the present invention;

[0045] Figure 2 The array element excitation and array factor patterns are obtained by the low sidelobe pattern synthesis method based on second-order cone programming.

[0046] in, Figure 2 (a): Array element excitation pattern, Figure 2 (b): Array factor pattern;

[0047] Figure 3 This invention provides the array element excitation and array factor pattern obtained without dynamic range constraints.

[0048] in, Figure 3 (a): Array element excitation pattern, Figure 3 (b): Array factor pattern;

[0049] Figure 4 The array element excitation and array factor pattern obtained under the 12dB dynamic range constraint of this invention;

[0050] in, Figure 4 (a): Array element excitation pattern, Figure 4 (b): Array factor pattern; Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] I. Explanation and Description of Embodiments. To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanation and description of the embodiments that expand upon the technical solutions of the claims.

[0053] This invention addresses the problems of complex optimization models and low computational efficiency in existing low-sidelobe pattern synthesis methods based on convex optimization, as well as the constraints on the dynamic range of array elements in engineering applications, such as system hardware specifications and link budgets. It proposes a dynamically adjustable low-sidelobe pattern synthesis method based on linear programming. This method utilizes the structural symmetry of the array layout to simplify the representation of array factors, thereby significantly reducing the dimensionality of the optimization problem and the number of constraints. Furthermore, it introduces upper and lower bound constraints on the array element weights, thus achieving dynamically adjustable array element weights and improving the aperture efficiency of the antenna array. The following is a combination of... Figure 1A detailed description of a low sidelobe pattern synthesis method provided by embodiments of the present invention includes the following steps:

[0054] S1: Determine the symmetry of the array structure to be optimized, and pre-set the starting polar angle θ of the sidelobe region to be optimized according to the array aperture. sl The initial upper bound α0 and initial lower bound β0 of the array element weights are set according to the dynamic range of the radio frequency link. The array element weights are the array element excitations, referring to the excitation coefficients at the antenna ports.

[0055] S2: Obtain the structural symmetry of the antenna array to be optimized, simplify the array factor response expression based on the structural symmetry and represent it in vector form to obtain the simplified array factor response expression.

[0056] The array factor response expression is simplified based on the array structure symmetry, reducing the sidelobe optimization range. The array factor refers to the relative radiation intensity of the array in a given direction, which depends on the excitation coefficients of each array element. The array factor is obtained by taking the inner product of the vector composed of the element weights and the steering vector in the given direction. For an array with 2M+1 elements and a centrally symmetric layout, under amplitude-weighted optimization, its angular response... The matrix factor response expression is shown below:

[0057]

[0058] in,

[0059]

[0060] In the formula, Let k be the array factor pattern expression for a centrosymmetric array. x and k y There are two wavenumber components, where λ is the wavelength corresponding to the array's operating frequency, (x m ,y m ) represents the position of the m-th array element, w0 is the excitation coefficient of the central array element, and θ is the polar angle. For azimuth, w m Let be the excitation coefficient of the m-th array element;

[0061] In a centrosymmetric array, the sidelobe region to be optimized is θ sl The initial polar angle of the sidelobe region to be optimized;

[0062] For an array with 4M+1 cells and a mirror-symmetric layout, under amplitude-weighted optimization, its angle... The matrix factor response expression is shown below:

[0063]

[0064] In a mirror-symmetric array, the sidelobe region to be optimized is:

[0065] The simplified matrix factors are represented in vector form:

[0066]

[0067] In the formula, h n Let w be the guiding vector in the nth direction, and w be the simplified array element weight vector.

[0068] S3: The low sidelobe pattern synthesis problem is modeled as a linear programming problem, and its optimization model is as follows:

[0069]

[0070] α0≤w m ≤β0,θm=0,···,M;

[0071]

[0072] In the formula, Let g be an M+1 dimensional vector composed of positive real numbers, g be the maximum reduced matrix factor response of the discretized sidelobe region to be optimized, and N be the number of sampling points in the sidelobe region. The main lobe response is defined by α0 as the initial upper bound of the element weights and β0 as the initial lower bound of the element weights.

[0073] Since this problem is a linear optimization problem, and the dimension of the optimization variables and the number of constraints in the sidelobe region are significantly reduced compared to direct modeling, the computational efficiency of this problem is high, and the convex problem modeling ensures the optimality of the solution.

[0074] S4: Determine whether the array element excitations meet the system EIRP or G / T value index based on the solution of the linear programming problem. If not, adjust the upper and lower bounds α and β of the array element weights and solve again until the system index is met.

[0075] S5: Output the optimized array element weights and corresponding radiation patterns. At a given angle, the path difference of the array elements varies due to their different arrangement positions. The steering vector is then calculated, and the dot product of the steering vector and the array element weight vector is the array factor response in that direction. The responses in different directions constitute the radiation patterns.

[0076] II. Evidence of the Relevant Effects of the Embodiments. The embodiments of the present invention have achieved some positive effects during research and development or use, and indeed possess significant advantages compared to existing technologies. The following content... Figures 2-4 Describe it.

[0077] The effects of the present invention are achieved through Figures 2-4Note: The array used in the simulation experiment is a 32×32 square grid array with a mirror-symmetric layout and half-wavelength spacing. The preset starting polar angle of the sidelobe region is θ. sl =5°, Figure 2 (a) shows the optimized array element weights obtained by the low sidelobe pattern synthesis method based on second-order cone planning. It can be seen that the weights of array elements at different positions vary greatly. Figure 2 (b) is its array factor pattern, where the peak sidelobe in the visible area is approximately -31.11 dB, and the maximum peak sidelobe during scanning is -23.48 dB. During scanning, there will be a significant increase in sidelobe, which will reduce the system performance.

[0078] Figure 3 This invention presents the optimization results obtained under the condition that the element weights have no dynamic range constraints. Figure 3 (a) Array element weight distribution diagram and Figure 2 As can be seen from the comparison in (a), the array has significantly more full-power output units than the optimization results of the low sidelobe pattern synthesis method based on second-order cone planning, and the array aperture efficiency is higher. Figure 3 (b) is its array factor pattern, and its peak sidelobe in the visible area is -27.79dB, while the maximum sidelobe during scanning is -27.43dB, with no significant rise.

[0079] Figure 4 The optimized result obtained by this invention under the constraint of a 12dB dynamic range for element weights is as follows: the maximum element weight is 0dB, the minimum is -12dB, the peak sidelobe within the visible area is -25.69dB, and the maximum sidelobe during scanning is -25.24dB, achieving a trade-off between hardware dynamic range constraints and array scanning performance. Comparing this invention with a low-sidelobe pattern synthesis method based on second-order cone programming, it can be found that this invention can obtain easy-to-use excitation coefficients and a more ideal pattern, which can greatly improve the performance during array scanning. Furthermore, while considering the low sidelobe requirement, it also takes into account the constraints of system hardware specifications and link budget on the element dynamic range, improving the aperture efficiency of the antenna array and enhancing system robustness.

[0080] Based on the same concept, the present invention also provides a low sidelobe pattern synthesis device, including a setting module, a simplification module, a construction module and a solution module.

[0081] The configuration module is used to set the initial upper and lower bounds of the array element weights based on the dynamic range of the RF link of the antenna array to be optimized.

[0082] The simplification module is used to obtain the structural symmetry of the antenna array to be optimized. Based on the structural symmetry, the array factor response expression is simplified and represented in vector form to obtain the simplified array factor response expression.

[0083] The building module is used to model the low sidelobe pattern synthesis problem as a linear programming problem with the optimization objective of minimizing the maximum simplified array factor response at discrete sampling points in the sidelobe region and with the constraints of maintaining the initial upper and lower bounds of the main lobe gain and array element weights of the antenna array to be optimized.

[0084] The solution module is used to solve the linear programming problem, obtain the optimized element weights and the corresponding array factor radiation pattern, and obtain the equivalent isotropic radiated power (EIRP) or G / T value of the antenna array system based on the optimized element weights. If the EIRP or G / T value does not meet the corresponding set value, the initial upper and lower bounds of the element weights are adjusted, and the linear programming problem is solved again; if it meets the set value, the optimized element weights and the corresponding array factor radiation pattern are output.

[0085] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described low sidelobe pattern synthesis method.

[0086] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned low sidelobe pattern synthesis method.

[0087] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0088] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A low sidelobe pattern synthesis method, characterized in that, Includes the following steps: Set the initial upper and lower bounds of the array element weights based on the dynamic range of the RF link of the antenna array to be optimized; The structural symmetry of the antenna array to be optimized is obtained. Based on the structural symmetry, the array factor response expression is simplified and represented in vector form to obtain the simplified array factor response expression. The low sidelobe pattern synthesis problem is modeled as a linear programming problem, with the optimization objective being to minimize the maximum simplified array factor response at discrete sampling points in the sidelobe region and the constraints being to maintain the initial upper and lower bounds of the main lobe gain and array element weights of the antenna array to be optimized. Solve the linear programming problem to obtain the optimized array element weights and the corresponding array factor radiation pattern. Obtain the equivalent isotropic radiated power (EIRP) or G / T value of the antenna array system based on the optimized array element weights. If the EIRP or G / T value does not meet the corresponding set value, adjust the initial upper and lower bounds of the array element weights and solve the linear programming problem again. If satisfied, output the optimized array element weights and the corresponding array factor pattern; The process of obtaining the structural symmetry of the antenna array to be optimized, simplifying the array factor response expression based on the structural symmetry, and representing it in vector form to obtain the simplified array factor response expression includes the following steps: For the number of units is An array with a layout that satisfies central symmetry, under amplitude-weighted optimization, exhibits angular... The expression for the array factor pattern on the array is shown below: ； in, ； ； In the formula, Let be the array factor pattern expression for a centrally symmetric array. and For two wavenumber components, The wavelength corresponding to the array's operating frequency. For the first The position of each array element The excitation coefficient of the central array element. Polar angle, It is the azimuth angle. For the first The excitation coefficient of each array element; In a centrosymmetric array, the sidelobe region to be optimized is ; The initial polar angle of the sidelobe region to be optimized; For the number of units is An array with a layout that satisfies mirror symmetry, under amplitude-weighted optimization, exhibits angular... The expression for the array factor pattern on the array is shown below: ； In a mirror-symmetric array, the sidelobe region to be optimized is: ; The simplified matrix factors are represented in vector form: ； In the formula, To simplify the matrix factor response expression, For the first Guide vectors in each direction, The simplified array element weight vector; The low sidelobe pattern synthesis problem is modeled as a linear programming problem, as detailed below: In the formula, Composed of positive real numbers dimensional vector, The maximum simplified matrix factor response after discretization of the sidelobe region to be optimized. The number of sampling points in the sidelobe region. Main lobe response, This is the initial upper bound for the array element weights. This is the initial lower bound for the array element weights.

2. The low sidelobe pattern synthesis method as described in claim 1, characterized in that, The output of the optimized array element weights and corresponding array factor pattern includes the following steps: Obtain the guide vectors in different directions; By inputting the steering vectors in different directions and the optimized array element weights into the simplified array factor response expression, array factor pattern at different angles can be obtained.

3. A synthesis apparatus based on the low sidelobe pattern synthesis method of claim 1, characterized in that, include: The setting module is used to set the initial upper and lower bounds of the array element weights based on the dynamic range of the RF link of the antenna array to be optimized. The simplification module is used to obtain the structural symmetry of the antenna array to be optimized. Based on the structural symmetry, the array factor response expression is simplified and represented in vector form to obtain the simplified array factor response expression. The module is constructed with the optimization objective of minimizing the maximum simplified array factor response at discrete sampling points in the sidelobe region, and with the constraints of maintaining the initial upper and lower bounds of the main lobe gain and array element weights of the antenna array to be optimized, and the low sidelobe pattern synthesis problem is modeled as a linear programming problem. The solution module is used to solve the linear programming problem, obtain the optimized array element weights and the corresponding array factor radiation pattern, and obtain the equivalent isotropic radiated power (EIRP) or G / T value of the antenna array system based on the optimized array element weights. If the EIRP or G / T value does not meet the corresponding set value, the initial upper and lower bounds of the array element weights are adjusted, and the linear programming problem is solved again; if it meets the set value, the optimized array element weights and the corresponding array factor radiation pattern are output.

4. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the low sidelobe pattern synthesis method according to any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the low sidelobe pattern synthesis method according to any one of claims 1-2.

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