两级递阶遗传算法优化稀布直线阵列

非均匀稀布直线阵列通常具有较高的旁瓣,为了降低旁瓣,对传统遗传算法进行改进用于阵列优化。为了更好的解决早熟收敛问题,利用两级递阶遗传算法THGA（Two-level Hierarchic Genetic Algorithm）在各阵列电流激励幅度相同的情况下对阵元位置进行了优化,最大旁瓣降低至-15．2498dB。在各阵列电流激励幅度不同的情况下,提出了“微调”“粗调”的思想对激励和阵元位置同时进行优化。仿真结果表明,对于一个孔径为50λ的25个阵元组成的稀布直线阵列,可获得了更低的旁瓣,最大旁辩可降至-22．5955dB。     

基于改进遗传算法的圆阵稀布方法

由于圆形阵列所具有的特性,使其正得到日益广泛的应用,但是圆阵方向图却具有相对主瓣较高的旁瓣电平。为此,针对稀布圆形阵列的天线单元使旁瓣电平尽量降低的问题,应用改进的遗传算法,取角度差值为染色体的基因,进行阵列孔径、单元个数、最小间隔一定的稀布优化排列,减小了遗传算法的搜索空间,提高了搜索效率。仿真结果表明:该方法能有效提高收敛速度、降低圆阵的旁瓣电平。     

基于改进遗传算法的圆阵列方向图联合优化

为降低圆阵列方向图的峰值旁瓣电平,提出一种基于改进遗传算法的圆阵列方向图优化方法。将阵元位置和阵元权值作为联合优化变量,以最小化波束方向图峰值旁瓣为目标函数,采用遗传算法优化阵元位置和阵元权值,以增加变量的自由度,在采用双重选择机制的基础上,结合差分进化、内插/外推、单点交叉和多点交叉4种方式实现交叉变异。实验结果表明,该方法能降低陷入局部最优点的概率,具有较好的适应度和较快的收敛速度,使峰值旁瓣电平降低至?12.611 dB。     

基于改进差分进化算法的天线阵列优化研究

针对具有阵元数目、孔径尺寸以及最小阵元间距约束,以抑制峰值旁瓣电平(PSLL),使非均匀阵列单元得到优化布局.优化过程中由于搜索空间较大,优化结果可能出现不可行解.为有效地解决以上两个问题,提出了一种改进的差分进化算法,采用实值编码,通过使用新的个体描述方式,减小了优化搜索空间;同时通过改造适应度函数,避免了变异交叉后出现不可行解.给出了改进算法的流程,通过仿真实例验证了算法的有效性和稳健性.仿真结果表明,改进方法在有最小阵元间距约束下以抑制峰值旁瓣电平为目的的非均匀阵列单元优化布局问题中有良好的应用前景.     

基于遗传算法的圆柱阵列稀疏方法

由于共形阵列所具有的特性,使其正得到日益广泛的应用,但其耗用较多的阵元,方向图具有相对主瓣较高的旁瓣电平。为此,针对基本的共形阵列——圆柱阵列的天线阵元,应用经典遗传算法,以阵元的工作状态为优化参量,对其进行稀疏。减少了阵元数量,仿真结果表明,该方法能有效地降低圆柱阵列的旁瓣电平。     

改进狼群算法用于多约束稀布线阵综合

结合量子理论提出了一种改进狼群算法,并将其用于优化多约束稀布直线阵列综合问题。新算法通过量子位特殊编码方式、停滞检测与选择性变异极大地提高了全局优化能力。给出了改进狼群算法流程,并在给定阵列孔径和阵元数的条件下,实现了任意最小阵元间距约束下,抑制天线峰值旁瓣电平（PSLL）的稀布线阵综合仿真。通过解空间变换,有效避免了算法进行阵列综合时,狼群位置更新过程中出现不可行解问题,减少了判断步骤,提高了优化效率。通过典型实例的仿真对比,证实了该方法的有效性和稳健性,而且能获得比现有方法更低的PSLL和更高的优化效率。     

遗传算法用于波达方向估计的圆阵稀疏优化

关于阵列信号优化问题,利用遗传算法对双圈圆阵进行优化,尽量用最少的阵元个数达到甚至取得更好的 DOA 估计,改善了 CBF 算法的方位估计性能.在单源和多源入射条件下,分别设定了不同的目标函数,为了探测阵的主瓣宽度或最大旁瓣水平尽可能小,对双圈圆阵进行了稀疏优化.仿真结果表明:优化阵的主瓣宽度比原始均匀圆阵阵小 1°,最大旁瓣水平降低近 3dB 左右.优化后的阵列在不同频率和低信噪比情况下仍具有较好的 DOA 估计性能,为设计提供了可靠的参考.     

最小冗余MIMO雷达阵列设计

阵列天线的优化设计对降低系统成本、提高系统性能起着关键的作用。最小冗余阵因其能用较少的物理阵元形成较大的阵列孔径而备受关注,但是其阵元位置往往只能通过穷举法搜索得到,而穷举法的运算复杂度很高,因此不利于实际应用。针对这一不足,根据组合设计理论中的循环差集的基本原理,提出了一种基于循环差集的最小冗余MIMO雷达阵列设计算法。该算法利用最少的物理阵元能够得到更大的阵列孔径,并且能解析地求出发射阵元和接收阵元的位置,适用于任意大阵元数情形的阵列优化。仿真结果表明,该算法是一种有效的最小冗余MIMO雷达阵列优化算法。     

基于改进风驱动优化算法的稀疏阵列旁瓣抑制方法

针对阵列稀疏通常会引起波束方向图旁瓣电平抬高的问题,本文提出了一种基于改进风驱动优化算法和凸优化的稀疏阵列旁瓣抑制方法。该方法首先针对传统的风驱动优化（Wind driven optimization, WDO）算法缺少普遍适用的参数设置方案,提出了一种结合高斯分布的改进风驱动优化算法。将半圆阵方向图的峰值旁瓣电平作为目标函数,采用改进的WDO算法作为全局优化算法来优化阵元位置,同时采用凸优化算法作为局部优化算法来高效求解有效阵元的最优加权系数,确保了阵元位置和权值的理想匹配。仿真结果表明,在稀疏 阵列阵元数一定的情况下,算法可有效降低旁瓣水平,具有更好的全局寻优能力和更快的收敛速度,为稀疏阵列的旁瓣抑制提供了有效的优化设计方法。     

遗传算法用于波达方向估计的线阵优化

利用遗传算法优化阵列设计来改善声目标波达方向估计性能.研究波达方向估计采用信号相位匹配原理的奇异值分解法SVDSPM,利用遗传算法对线阵进行了优化.优化时,阵列孔径不变,将阵元数目作为优化变量,优化目标是降低DOA估计误差.仿真结果标明:优化后在阵元数目减少的情况下,DOA估计偏差和均方误差更小.而且优化的阵列有好的低信噪比及宽带信号波达方向估计的能力.     

增加主瓣增益控制的阵列天线方向图综合遗传算法

阵列天线方向图综合是针对阵列天线方向图进行的相关研究中的一项基础性的研究内容。不同的应用场景中不仅需要设计形式不同的天线阵列,更需要阵列天线方向图并根据不同的性能参数指标要求进行分析优化,从而获得符合使用要求的阵列方向图。针对传统阵列天线方向图综合算法在方向图综合过程中未考虑主瓣增益约束的问题,提出了一种用于阵列天线方向图综合的改进遗传算法。在经典遗传算法的基础上设计了新的适应度函数以满足特殊情况下对主瓣增益进行约束的需求;并在算法计算过程中增加了精英保留策略,设计了随进化代数改变的交叉、变异概率。经仿真实验,结果表明所提算法能够在阵列天线方向图综合时在保证主瓣方向上的增益满足要求的同时可以获得相对低的副瓣电平水平。     

基于微遗传算法的稀疏天线阵列优化方法

为生成无栅瓣、高空间分辨率的方向图,均匀平面阵列使用的天线单元数量很多,实现难度和成本高。稀疏布阵只需要相对较少的单元数目,会出现旁瓣电平升高、测向模糊等问题。通过对天线阵列特性进行分析,以全向一致、无栅瓣、低旁瓣的高分辨方向图为目标,利用微遗传算法对多重圆环阵列单元的位置参数进行优化,性能分析表明综合出的多重圆环阵列具有方位角对称、旁瓣电平低、起伏小的特点,且该算法优化效率高、收敛速度快。     

A trade-off curve computation for linear antenna arrays using an evolutionary multi-objective approach

This paper deals with the main beam width and the maximum minor lobes height trade-off curve approximate computation for linear antenna arrays. Antenna elements with uniform and non-uniform separations are considered. A well-known genetic algorithm is proposed as the methodology for the computation. For the uniform separation case the proposed algorithm is validated against an exact method based on Chebychev polynomials. Experimental results show that non-uniform separation arrays with uniform current excitation outperform uniform separation arrays. This outperformance is given under the criteria of main beam width and height of the maximum minor lobe.     

Circular antenna array synthesis using firefly algorithm

In this article, the design of circular antenna arrays (CAAs) and concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the firefly algorithm (FA) is used to determine an optimum set of weights and positions for CAAs, and an optimum set of weights for CCAAs, that provides a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The FA represents a new algorithm for optimization problems in electromagnetics. It is shown that the FA results provide a SLL reduction that is better than that obtained using well‐known algorithms, like the particle swarm optimization, genetic algorithm (GA), and evolutionary programming. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:139–146, 2014.     

Synthesis of thinned concentric circular antenna arrays using teaching‐learning‐based optimization

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the teaching‐learning‐based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:443–450, 2014.     

MIMO雷达正交波形集设计——线性调频-相位编码混合波形

提出了基于线性调频-相位编码组合波形的MIMO雷达正交波形集设计方法。与常规单载频子脉冲相位编码信号相比,线性调频-相位编码波形具有易于实现宽频带和高距离分辨率的优点;建立了MIMO雷达正交波形集优化模型,利用遗传算法进行组合优化,所得波形集不仅具有较低的互相关性,而且每个波形的自相关旁瓣也受到一定的抑制,波形集性能优于常规相位编码波形。仿真实验验证了理论分析的正确性和优化方法的有效性。     

Design of aperiodic planar arrays for isoflux radiation in GEO satellites by applying evolutionary optimization

The design of an aperiodic planar array is presented in this research. This design of aperiodic arrays considers the reduction of the side lobe level and the isoflux radiation requirements for GEO (Geostationary Earth Orbit) satellite applications. In this way, it is considered four different optimization cases. The first two cases are optimizations of amplitude and phase excitations for the antenna elements in a uniform antenna array and the last two cases are optimizations of positions of the antenna elements and certain number of levels of amplitude excitation in an aperiodic array. In this case, it is proposed a simple new approach combining the main idea of both thinned theory and random arrays approaches. For this optimization problem, the well-known method of Genetic Algorithms (GA) is utilized. The obtained results show the proper performance of the array factor to provide the isoflux radiation and low side lobe level. Depending on the performance requirements, the design of the aperiodic array could lead the satellite hardware to be reduced significantly even more that results presented previously in the literature.     

基于仿真退火算法的任意阵列宽带波束形成

提出了基于仿真退火算法优化思想的任意宽带波束设计方法,其期望响应由工作频带划分成的等间隔窄子带上旁瓣波束形成权系数构成。各个子带上的低旁瓣波束则采用基于仿真退火波束形成原理的波束优化设计方法得到。借助计算机对二十四元圆阵进行波束优化设计。结果表明,使用上述方法设计的波束与常规方法相比,获得了更低的旁瓣级和更稳定束宽,充分说明了方法的可行性和有效性。     

Design of planar concentric circular antenna arrays with reduced side lobe level using symbiotic organisms search

This paper investigates the design of concentric circular antenna arrays (CCAAs) with optimum side lobe level reduction using the Symbiotic Organisms Search (SOS) algorithm. Both thinned and full CCAAs are considered. SOS represents a rather new evolutionary algorithm for antenna array optimization. SOS is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. SOS uses simple expressions to model the three common types of symbiotic relationships: mutualism, commensalism, and parasitism. These expressions are used to find the global minimum of the fitness function. Unlike other methods, SOS is free of tuning parameters, which makes it an attractive optimization method. The results obtained using SOS are compared to those obtained using several optimization methods, like Biogeography-Based Optimization (BBO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Programming (EP). It is shown that the SOS is a robust straightforward evolutionary algorithm that competes with other known methods.