基于多目标粒子群算法的小型化槽螺旋天线研究

设计了一种小型化的槽螺旋天线。首先在普通的槽螺旋天线臂上开槽,再在天线最外圈的天线臂上加载6个等距分布的贴片电阻,基于多目标粒子群算法对开槽位置和贴片电阻阻值进行优化,使得天线的工作频率向低频偏移,同时保证具有良好的圆极化辐射特性。仿真结果表明:在0.44~18.00 GHz频段内回波损耗小于–10 d B,1.8~18.0 GHz频段内轴比小于3dB。与普通的螺旋天线相比,在天线口径不变的情况下,极大地改善了天线的低频端的辐射特性。     

粒子群优化算法用于阵列天线方向图综合设计

粒子群优化算法是基于一群粒子的智能运动而产生的一类随机进化算法,其优点是算法非常利于理解和应用.本文介绍了粒子群算法的原理和流程,研究了如何将这种方法运用于天线阵的方向图综合上,给出了PSO算法在综合阵列方向图的应用实例,表明粒子群算法在天线阵列综合中具有广泛的应用前景.     

基于改进粒子群算法的共形阵列天线综合

共形阵列天线的赋形方向图综合涉及大量的运算,成为现在研究的一大难点,目前对共形阵方向图综合的研究比较少,且所用算法存在理论复杂、耗时长的缺点。文中运用改进粒子群算法对圆环阵、圆柱阵方向图的综合进行了研究,仿真结果表明,改进粒子群算法能够较快地形成期望方向图,证明了该方法的有效性和实用性。     

一种基于改进粒子群的多小区天线联合优化算法

针对基站天线参数调优问题,提出了一种用于在庞大离散空间中搜索最优解的优化算法,该算法借鉴了粒子群算法的基本概念,分为三个步骤.第一步为随机筛选,挑选出一批较为优秀的个体组成粒子群;第二步为大步长调节,通过大范围搜索提高寻优效率;第三步为小步长调节,在迭代时只做细微调节进行局部查找,最终能找到最优解.通过直接操作场强矩阵...     

基于改进粒子群算法的天线方向图综合技术

针对基本粒子群算法的早熟收敛、易收敛于局部极值的特点，提出一种改进的粒子群优化算法，采用对全局最佳微扰和惯性权重跳变阈值的设置改善了算法的优化速度和收敛精度。经过对一系列测试函数的计算，证明该方法具有良好的优化效果。最后，给出了该方法应用于阵列天线方向图综合中的模型和仿真实例。     

基于改进粒子群算法的天线方面图综合技术

针对基本粒子群算法的早熟收敛、易收敛于局部极值的特点,提出一种改进的粒子群优化算法,采用对全局最佳微扰和惯性权重跳变阈值的设置改善了算法的优化速度和收敛精度.经过对一系列测试函数的计算,证明该方法具有良好的优化效果.最后,给出了该方法应用于阵列天线方向图综合中的模型和仿真实例.     

基于正交粒子群算法的PIFA天线宽频带优化设计

伴随着目前个人移动通讯技术的迅速发展,移动通信设备对小型化、多频带和宽频带的天线的要求越来越迫切。许多的天线设计都依赖于优化技术,由于天线的结构参数很多,无法给出解析式函数,所以给优化问题提出了新的挑战。文章主要内容是把正交粒子群算法运用到PIFA天线结构的宽频带优化设计。     

基于一种新的粒子群算法的天线方向图综合技术

该文在线性递减权重粒子群算法的基础上提出了一种新的准粒子群优化算法。新算法借鉴了遗传算法中交叉的思想并采用了对偶算法模型改善了算法的优化速度和收敛特性。仿真表明,将此算法应用在天线方向图综合中,取得了良好的优化效果。     

基于粒子群算法实现相控阵天线波束赋形

本文利用改进后的粒子群算法实现了相控阵天线的波束赋形,赋形后的波束形状满足设计要求。     

基于改进粒子群算法的标签天线结构参数多目标优化设计

为了解决天线设计人员应用电磁仿真软件优化天线结构时存在的优化方向不明确和优化速度慢的问题,文中以干式水表的嵌入式射频识别标签天线设计为例,提出了基于改进粒子群算法的标签天线结构参数多目标寻优方法。首先,根据干式水表产品追溯需求,提出了中心频点尽可能接近理想中心频点、回波损耗尽可能低、带宽尽可能宽、面积尽可能小的四个目标函数。其次,为避免粒子群算法陷入局部最优,采用多维均匀拉丁超立方初始化、Logistic 混沌映射非线性变化惯性权重、网格划分变化学习因子、高斯扰动策略等方法对算法进行改进,并应用于标签天线结构参数多目标优化中。最后,进行了实例验证。验证结果表明:利用改进后的粒子群算法得到的标签天线结构参数优化结果可更大程度满足优化目标需求,优化耗时仅为电磁仿真软件的40.1%。     

Optimized radii and excitations with concentric circular antenna array for maximum sidelobe level reduction using wavelet mutation based particle swarm optimization techniques

In this paper, a Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach with Wavelet Mutation (PSOCFIWA-WM) is applied to the process of synthesizing three-ring Concentric Circular Antenna Arrays (CCAA) without and with central element feeding, focused on maximum sidelobe level reductions. Sidelobe level (SLL) is a critical radiation pattern parameter in the task of reducing background noise and interference in the most recent wireless communication systems. To improve the radiation pattern with maximum SLL reduction, an optimum set of antenna element parameters as excitation weights and radii of the rings are to be developed. The extensive computational results show that the method of PSOCFIWA-WM provides a maximum sidelobe level reduction of 96.06% with respect to the uniformly excited case for the particular CCAA containing 4, 6, and 8 numbers of elements in the three successive rings along with central element feeding.     

数字阵列天气雷达低副瓣天线设计

为天气雷达设计一维相扫相控阵天线,方位面副瓣低于-30 d B,波束宽度小于1°,俯仰面接收副瓣低于-40 d B。方位面波束采用窄边波导裂缝阵列天线实现,采用全数字T/R组件精确控制幅度和相位,实现低于-40 d B的俯仰面接收副瓣。测试结果表明,副瓣电平与波束宽度指标与理论值吻合较好,满足指标要求。     

Linear antenna array synthesis using cat swarm optimization

Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.     

The effect of discrete scatterers on a low sidelobe antenna

The effect of randomly distributed scatterers placed in the far field of an antenna is explored. A technique for predicting the effects of specular scatterers on antenna performance has been developed. Laboratory testing of a low sidelobe antenna indicated an acceptable low sidelobe level. However, when the antenna was field tested, it exhibited average radiated power levels off the main beam that were significantly greater than those anticipated. In an effort to determine the cause of this increase, a computer model of an antenna in a field site has been developed, which includes the calculation of the total scattered power of the antenna main beam and sidelobes from specular reflectors distributed over the far field of the antenna.     

Transmitter antenna placement in indoor environments using particle swarm optimisation

The aim of this article is to suitably locate the minimum number of transmitter antennas in a given indoor environment to achieve good propagation coverage. To calculate the electromagnetic field in various points of the environment, we develop a software engine, named ray-tracing engine (RTE), in Matlab. To achieve realistic calculations, all parameters of geometry and material of building are considered. Particle swarm optimisation is employed to determine good location of transmitters. Simulation results show that a full coverage is obtained through suitably locating three transmitters.     

基于量子粒子群改进算法的直线阵综合

提出了一种基于反向学习机制的改进量子粒子群算法,采用反向学习机制增加种群的多样性,使搜索效率有了较大的提高,有效地避免了算法早熟收敛。应用这种改进算法成功地实现了直线阵的方向图综合,三个实例仿真结果证明了本文改进算法求解此类问题的有效性。     

Design of very thin wide band absorbers using modified local best particle swarm optimization

A method of using particle swarm optimization (PSO) algorithm to design electromagnetic absorber is presented. To demonstrate effectiveness of the PSO algorithm three different design cases are optimized. To reduce the local minimum traps, a modified local search strategy is employed. Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO). The results clearly show that the MLPSO is a robust, fast, and useful optimization tool for designing absorbers. A seven-layer absorber achieved by this method has reflection coefficient below 18.7 dB from VHF to 20 GHz.     

中心粒子群优化算法

在线性递减权重粒子群优化算法(LDWPSO)中提到了中心粒子这一概念,进而提出了中心粒子群优化算法(中心PSO)。在线性递减权重粒子群优化算法中,中心粒子不像其它一般的粒子,中心粒子没有明确的速度,并且被始终置于粒子群的中心。此外,在神经网络训练算法中比较中心粒子群优化算法和线性递减权重粒子群优化算法,结果表明:中心粒子群优化算法的性能优于线性递减权重粒子群优化算法。