Worst-case tolerance analysis on array antenna based on chaos-genetic algorithm

This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic algorithm （CGA） is proposed. The proposed method utilizes chaos to optimize initial population for the genetic algorithm （GA） and introduces chaotic disturbance into the genetic mutation, thereby improving the ability of the GA to search for the global optimum. Numerical simulations demonstrate that the accuracy and stability of the worst-case analysis of the proposed approach are superior to the GA. And the proposed algorithm can be used easily for the error tolerant design of antenna arrays.

Adaptive Maxwell’s Equations Derived Optimization and Its Application in Antenna Array Synthesis

In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis.

Central Force Optimization with Gravity <0, Elitism, and Dynamic Threshold Optimization: An Antenna Application, 6-Element Yagi-Uda Arrays

This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those extensions are Negative Gravity, Elitism, and Dynamic Threshold Optimization. The basic CFO heuristic does not include any of these, but adding them substantially improves the algorithm’s performance. This paper extends the work reported in a previous paper that considered only negative gravity and which showed a significant performance improvement over a range of optimized arrays. Still better results are obtained by adding to the mix Elitism and DTO. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6- element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimization problem, antenna or not, utilizing Central Force Optimization as the Global Search and Optimization engine will benefit by including all three extensions, probably substantially.

Analysis of electrical performances of planar active phased array antennas with distorted array plane

The influence of the distorted plane of the active phased array antenna on the electromagnetic performance is of great significance to the research on and development of the high-performance antennas. On the bent and bowl-shape distortion, the model is established of the relationship between the electromagnetic performance and the position error of the radiated elements. The method is presented of analyzing the far-field pattern of the distorted rectangular active phased array antenna. The analysis results of a planar phased array antenna with different distortions grades prove the validity of the model. Therefore, by the method, the antenna designers may set the reasonable requirement on the structural tolerance in manufacturing antenna.

Six-Element Yagi Array Designs Using Central Force Optimization with Pseudo Random Negative Gravity

A six-element Yagi-Uda array is optimally designed using Central Force Optimization (CFO) with a small amount of pseudo randomly injected negative gravity. CFO is a simple, deterministic metaheuristic analogizing gravitational kinematics (motion of masses under the influence of gravity). It has been very effective in addressing a wide range of antenna and other problems and normally employs only positive gravity. With positive gravity the six element CFO-designed Yagi array described here exhibits excellent performance with respect to the objectives of impedance bandwidth and forward gain. This paper addresses the question of what happens when a small amount of negative gravity is injected into the CFO algorithm. Does doing so have any effect, beneficial, negative or neutral? In this particular case negative gravity improves CFO’s exploration and creates a region of optimality containing many designs that perform about as well as or better than the array discovered with only positive gravity. Without some negative gravity these array configurations are overlooked. This Yagi-Uda array design example suggests that antennas optimized or designed using deterministic CFO may well benefit by including a small amount of negative gravity, and that the negative gravity approach merits further study.

Accurate Angle-of-Arrival Measurement Using Particle Swarm Optimization

As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates.

Quantum-Inspired Equilibrium Optimizer for Linear Antenna Array

With the rapid development of communication technology,the problem of antenna array optimization plays a crucial role.Among many types of antennas,line antenna arrays(LAA)are the most commonly applied,but the side lobe level(SLL)reduction is still a challenging problem.In the radiation process of the linear antenna array,the high side lobe level will interfere with the intensity of the antenna target radiation direction.Many conventional methods are ineffective in obtaining the maximumside lobe level in synthesis,and this paper proposed a quantum equilibrium optimizer(QEO)algorithm for line antenna arrays.Firstly,the linear antenna array model consists of an array element arrangement.Array factor(AF)can be expressed as the combination of array excitation amplitude and position in array space.Then,inspired by the powerful computing power of quantum computing,an improved quantum equilibrium optimizer combining quantum coding and quantum rotation gate strategy is proposed.Finally,the proposed quantum equilibrium optimizer is used to optimize the excitation amplitude of the array elements in the linear antenna array model by numerical simulation to minimize the interference of the side lobe level to the main lobe radiation.Six differentmetaheuristic algorithms are used to optimize the excitation amplitude in three different arrays of line antenna arrays,the experimental results indicated that the quantum equilibrium optimizer is more advantageous in obtaining the maximum side lobe level reduction.Compared with other metaheuristic optimization algorithms,the quantum equilibrium optimizer has advantages in terms of convergence speed and accuracy.

Optimal array factor radiation pattern synthesis for linear antenna array using cat swarm optimization： validation by an electromagnetic simulator

In this paper, an optimal design of linear antenna arrays having microstrip patch antenna elements has been carried out Cat swarm optimization （CSO） has been applied for the optimization of the control parameters of radiation pattern of an antenna array. The optimal radiation patterns of isotropic antenna elements are obtained by optimizing the current excitation weight of each element and the inter-element spacing. The antenna arrays of 12, 16, and 20 elements are taken as examples. The arrays are designed by using MATLAB computation and are validated through Computer Simulation Technology-Microwave Studio （CST-MWS）. From the simulation results it is evident that CSO is able to yield the optimal design of linear antenna arrays of patch antenna elements.

Pattern synthesis of antennas based on a modified particle swarm optimization algorithm

In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.

Antenna Array Pattern Synthesis via Coordinate Descent Method

This paper presents an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM). With this algorithm, the complex element weights are found to minimize a weighted L2 norm of the difference between desired and achieved pattern. Compared with traditional optimization techniques, CDM is easy to implement and efficient to reach the optimum solutions. Main advantage is the flexibility. CDM is suitable for linear and planar array with arbitrary array elements on arbitrary positions. With this method, we can configure arbitrary beam pattern, which gives it the ability to solve variety of beam forming problem, e.g. focused beam, shaped beam, nulls at arbitrary direction and with arbitrary beam width. CDM is applicable for phase-only and amplitude-only arrays as well, and furthermore, it is a suitable method to treat the problem of array with element failures.

Adaptive Uniform Circular Array Synthesis Using Cuckoo Search Algorithm

Naturally suited array geometry for 360&deg; coverage is the uniform circular array (UCA). A comparison of two types of uniform circular array configurations is presented in this paper. Due to its symmetrical geometry UCA is always targeted which results in minimal change inside lobe levels and beam width when scanned by a phased array antenna. Particle Swarm Optimization and Cuckoo algorithm are used for the calculation of complex weights of the array elements. Comparisons are drawn in the context of adaptive beam forming capabilities. Obtained results suggest that planar uniform circular array (9:10) using Cuckoo algorithm, has better beam forming properties with also reduced side lobe levels when compared to other geometry.

Nelder-Mead Based Iterative Algorithm for Optimal Antenna Beam Patterns in Ad Hoc Networks

Directional antennas shape transmission patterns to provide greater coverage distance and reduced coverage angle. Use of adaptive directional antenna arrays can minimize interference while also being more energy efficient. When used in an ad-hoc network, this reduces interference among transmitting nodes and thereby increases throughput. Such “smart antennas” use digital beamforming based on signal processing algorithms to compute the appropriate weights to form effective antenna patterns. Smart antennas require the knowledge of the signal received at each antenna in the antenna array, thereby increasing the complexity of hardware and cost. Also, conventional smart antennas optimize results for each individual node, while it is preferable to have a global optimal solution. A problem that has not been addressed is how to compute individual beam patterns that maximize some measure of global network performance. Historically, the focus has been on finding node antenna patterns that give locally optimal performance. In this paper, we investigate a low hardware complexity beamforming approach aimed at improving global performance that uses average Noise-to-Signal ratio as the performance measure. Given a multi-hop route from source to destination, beam patterns are shaped to maximize average signal-to-noise ratio across all nodes on the route, which reduces bit-error rates and extends battery and network lifetime. The antenna weights are sequentially adjusted across all nodes in the route to achieve optimization across the network. By using phase-only weights, hardware costs are minimized. The performance of the algorithm using different path loss models is explored.

工艺条件约束下的再制造机电产品公差优化设计方法

针对再制造机电产品公差设计不合理造成的再制造成本高、产品质量与工艺稳定性差的问题,提出一种考虑工艺条件约束的再制造公差优化设计方法。通过关联分析再制造产品毛坯件来源、零部件构成以及工艺能力等工艺条件约束对公差设计的影响特征,构建由再制造产品公差设计阶段至制造阶段的四级公差分配体系与优化方法框架。在满足产品精度要求、工艺稳定性等设计目标基础上,以制造阶段成本,质量损失和工序过程能力作为优化目标,建立再制造公差设计多目标优化模型,提出基于再制造加工面精度价值的模型修正方法,并利用天牛须搜索算法进行求解寻优。以再制造齿轮箱公差设计为例对所提方法的有效性与实用性进行了验证。

构架天线四面体桁架单元精度分析与多目标优化设计

为了减小加工装配误差对天线桁架精度的影响,解决含随机变量的优化问题,综合考虑了杆件加工装配误差正态分布特性对精度的影响,通过最小势能原理求解得到了含杆件误差的构架天线桁架单元的平衡状态,采用蒙特卡洛方法对杆件误差变化对型面精度的影响开展了分析,得到杆件随机误差下的平均精度。为提高计算效率,建立了以杆件长度公差为设计变量、以桁架型面精度和加工成本为目标函数的代理模型,通过多目标遗传优化算法进行了公差优化设计,得到了代理模型误差小于8%的多组公差设计方案。其中平均精度最小达到了0.013 mm,并且分析结果显示腹杆影响显著大于底杆。

基于混沌粒子群算法的阵列天线容差分析

研究阵列天线性能优化设计,针对阵列天线幅相误差对天线性能的影响,传统的阵列天线容差分析采用统计理论方法,前提需要假设幅相误差服从某种概率分布,导致不同分布的幅相误差对应不同的结论形式,并且公式推导过程复杂。为了在建立阵列天线最坏情况容差分析的数学模型,提出了一种混沌粒子群算法简单实用的阵列天线最坏情况容差分析方法。通过在粒子群算法中利用混沌技术优化初始种群,并对位置更新时引入混沌扰动项和位置更新后进行边界约束,进行仿真,提高了粒子群算法的全局搜索能力。结果验证了算法在最坏情况分析时,可以得到在准确性和稳定性方面的优化结果。

容差参数的阵列天线综合法

本文研究了设计参数具有容差的阵列天线综合方法,给出了容差优化的数学模型和固定容差参数阵列天线的综合过程,并对容差优化的数学方法进行了讨论,最后给出了一计算例子。

容差参数的阵列天线方向图综合法

本文研究了设计参数具有容差的阵列天线方向图综合法;给出了阵列天线容差优化的数学模型;提出了求解容差优化问题的数学方法;建立了直线阵列天线容差区内误差任意分布的物理模型;并对激励幅度具有容差的侧射天线阵进行了计算。本文的方法可以最大限度地避免设计参数容差对天线性能的影响。

5G evolution promoting innovation of antenna systems

We introduce the basic concept,background,and development of mobile communication systems from the first generation(1G)to the fifth generation(5G)including their antenna systems.We also describe the requirements for 5G networking and optimization of antenna systems,and present the basic principle of three-dimensional array antennas.Weight optimization methods of massive multiple-input multiple-output(MIMO)antennas are proposed and verified.Finally,several ideas are given to solve the problem of power consumption of 5G antenna systems.