Antenna Array Synthesis Research Articles

Constrained Least-Squares Optimization in Conformal Array Antenna Synthesis

The least mean square error of the constrained least-squares optimization method is studied. The phases of non-zero goal values and the phases of non-zero constraint values can be selected by a fast iteration for optimum fit to the goal function amplitude. This kind of modified least-squares optimization method opens up a fast and robust synthesis method for large conformal antenna arrays. Constraints can be written also for the element excitation amplitudes and a phase synthesis with predefined amplitudes can be done. Iterative weight-correction technique is used for accurate sidelobe control. Synthesis examples of two-dimensional elliptical and large three-dimensional conformal antenna arrays are presented

SYNTHESIS OF THINNED LINEAR ANTENNA ARRAYS WITH FIXED SIDELOBE LEVEL USING REAL-CODED GENETIC ALGORITHM

In this paper, we propose an optimization method based on real-coded genetic algorithm (GA) with elitist strategy for thinning a large linear array of uniformly excited isotropic antennas to yield the maximum relative sidelobe level (SLL) equal to or below a fixed level. The percentage of thinning is always kept equal to or above a fixed value. Two examples have been proposed and solved with different objectives and with different value of percentage of thinning that will produce nearly the same sidelobe level. Directivities of the thinned arrays are found out and simulation results of different problems are also compared with published results to illustrate the effectiveness of the proposed method.

PHASE-ONLY AND AMPLITUDE-PHASE ONLY SYNTHESIS OF DUAL-BEAM PATTERN LINEAR ANTENNA ARRAYS USING FLOATING-POINT GENETIC ALGORITHMS

In this paper, we present a comparison study between phase-only and amplitude-phase synthesis of symmetrical dual-pattern linear antenna arrays using floating-point or real-valued genetic algorithms (GA). Examples include a sum pattern and a sector beam pattern. In the former, phase is only optimized with predetermined Gaussian amplitude distribution of fixed dynamic range ratio (|amax/amin|) and in the latter, both are optimized with less dynamic range ratio than the former and yet share a common amplitude distribution.

PHYSICAL THEORY FOR PARTICLE SWARM OPTIMIZATION

We propose an inter-disciplinary approach to particle swarm optimization (PSO) by establishing a molecular dynamics (MD) formulation of the algorithm, leading to a physical theory for the swarm environment. The physical theory provides new insights on the operational mechanism of the PSO method. In particular, a thermodynamic analysis, which is based on the MD formulation, is introduced to provide deeper understanding of the convergence behavior of the basic classical PSO algorithm. The thermodynamic theory is used to propose a new acceleration technique for the PSO. This technique is applied to the problem of synthesis of linear array antennas and very good improvement in the convergence performance is observed. A macroscopic study of the PSO is conducted by formulating a diffusion model for the swarm environment. The Einstein’s diffusion equation is solved for the corresponding probability density function (pdf) of the particles trajectory. The diffusion model for the classical PSO is used, in conjunction with Schrodinger’s equation for the quantum PSO, to propose a generalized version of the PSO algorithm based on the theory of Markov chains. This unifies the two versions of the PSO, classical and quantum, by eliminating the velocity and introducing position-only update equations based on the probability law of the method.

A Practical Array Pattern Synthesis Approach Including Mutual Coupling Effects

This paper introduces a new approach for the pattern synthesis of antenna arrays including mutual coupling effects, based on the combination of the differential evolution (DE) algorithm and the complex embedded element patterns, which can be obtained either by measurement or by full wave simulation of the array. The proposed approach has the advantage of taking into account many non-ideal effects as compared to other pattern synthesis approaches. Numerical results for the design of a 16-element printed dipole linear array with low sidelobe sum patterns and power patterns are presented to demonstrate the effectiveness of the proposed approach.

Non Uniform-Antenna Array Synthesis Using Neural Networks

Traditional antenna array synthesis methods are usually based on uniform arrays. A neural network can relate the radiation pattern with the voltages that must be applied to each radiating element without making any assumption about the geometry of the antenna, and allowing solving non uniform-antenna array problems even considering coupling effects.

Touring Ant Colony Optimization Algorithm for Shaped-Beam Pattern Synthesis of Linear Antenna

An efficient search technique based on a touring ant colony optimization algorithm is presented for the shaped-beam pattern synthesis of a linear antenna array. The element excitations of equally spaced array and the element positions of uniformly excited array are determined to closely approximate the desired pattern. In the touring ant colony optimization algorithm, the frequency memory feature of tabu search is used. Illustrative examples of cosecant, flat-topped and pencil beam patterns are given to verify the validity and accuracy of the technique.

Synthesis of Aperiodic Linear Phased Antenna Arrays Using Particle Swarm Optimization

In this article, the particle swarm optimization (PSO) algorithm is used to synthesize an optimal linear array. The technique is applied to synthesize a linear antenna array in the Chebyshev sense or to eliminate a grating lobe. This is may be achieved by optimizing the excitation currents and/or the relative locations of the array elements. These various design parameters are considered in this paper. In synthesizing equiripple radiation patterns, two approaches were used. The excitation currents feeding the array or the spacing between the array elements are optimized. It is to be noted that the desired equal side lobes level is achieved simultaneously with the narrowest possible beamwidth. Although the optimization problem may become nonlinear, convex, or nonconvex, especially if the interelement distances are the optimized parameters, it can be handled using the PSO algorithm. The PSO is simple to implement and does not require evaluation of gradients or coded parameters. In order to effectively utilize this algorithm, it is important to define an objective function that returns a single number to enable the PSO algorithm to minimize it. In this paper, the objective function is formulated to take into consideration both the lobe level and the main beam width. The results obtained using the particle swarm optimization technique are in excellent agreement with those available in the literature.

Quantum Particle Swarm Optimization for Electromagnetics

A new particle swarm optimization (PSO) technique for electromagnetic applications is proposed. The method is based on quantum mechanics rather than the Newtonian rules assumed in all previous versions of PSO, which we refer to as classical PSO. A general procedure is suggested to derive many different versions of the quantum PSO algorithm (QPSO). The QPSO is applied first to linear array antenna synthesis, which is one of the standard problems used by antenna engineers. The performance of the QPSO is compared against an improved version of the classical PSO. The new algorithm outperforms the classical one most of the time in convergence speed and achieves better levels for the cost function. As another application, the algorithm is used to find a set of infinitesimal dipoles that produces the same near and far fields of a circular dielectric resonator antenna (DRA). In addition, the QPSO method is employed to find an equivalent circuit model for the DRA that can be used to predict some interesting parameters like the Q-factor. The QPSO contains only one control parameter that can be tuned easily by trial and error or by suggested simple linear variation. Based on our understanding of the physical background of the method, various explanations of the theoretical aspects of the algorithm are presented

Exploitation of spatial channel model for antenna array synthesis

A technique to modify the radiation pattern of an antenna array in order to reject interference in a certain angular region is proposed. This technique exploits the knowledge of the spatial channel model, derived from recent measurements, to properly choose the position of a certain number of nulls. The method of projections is used to modify the excitation phases in order to synthesise the array pattern.

Analysis, synthesis, and diagnostics of antenna arrays through complex-valued neural networks

It is shown in this paper that when artificial neural networks are extended to be complex valued, they can be incorporated as a very powerful and effective tool in the analysis, synthesis, and diagnostics of antenna arrays. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1512–1515, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21706

SMART ANTENNA ARRAY PATTERNS SYNTHESIS: NULL STEERING AND MULTI-USER BEAMFORMING BY PHASE CONTROL

In this article, an efficient method for the pattern synthesis of the linear antenna arrays with the prescribed null and multi-lobe Beamforming is presented. Multi-lobe pattern and adaptive nulling of the pattern is achieved by controlling only the phase of each array element. The proposed method is based on the Sequential Quadratic Programming (SQP) algorithm and the linear antenna array synthesis was modelled as a multi-objective optimization problem. Multi-objective optimization is concerned with the maximization (or minimization) of a vector of objectives functions in the directions of desired signal that can be subject of a number of constraints (in our case, the constraints can be imposed as the null in the direction of interfering signal). To verify the validity of the technique, several illustrative examples of uniform excited array patterns with the main beam is placed in the direction of the useful signal and null is placed in the direction of potential interferers, and multi-beam patterns are demonstrated.

Synthesis of Circular Antenna Array Using Crossed Particle Swarm Optimization Algorithm

The particle swarm optimization (PSO) algorithm presents a new way for finding an optimal solution of complex optimization problems. In this paper a modified particle swarm optimization algorithm is applied to the optimization of the antenna array. We modify the PSO algorithm in some aspects. Firstly, the dynamic parameter is introduced to update the position equation, and the particles are limited in the search region. A new strategy for updating the speed is then adopted, in which the speed is weakened linearly. Based on these strategies, we proposed a new PSO algorithm named the crossed PSO algorithm. Simulation results show that the optimal pattern of the antenna array is able to approach the desired pattern. The results also demonstrate that the modified algorithm is superior to the original algorithm and the nonlinear least-square method.

High-accuracy neural-network-based array synthesis including element coupling

A novel neural-network (NN)-based technique for antenna array synthesis and a prototype of antenna array designed using it are presented. This technique is able to calculate the voltages that must be applied to the radiating elements considering all the physical properties of each element of the array and the coupling effects between all of them.

A Unification Procedure to the Analysis and Synthesis of Antenna Arrays

The far field pattern synthesis of arrays involves the calculation of an antenna array excitation factor, and this is performed in different ways depending on the adopted method. On the other hand, the determination of the array factor, when the excitation coefficients are known, is usually performed by a direct summation. This paper proposes a generalized procedure that can be applied to the analysis and to the synthesis of continuous and discrete arrays to evaluate the far field pattern. The proposed method employs the Fourier Relation, to derive the relationship between the array factor and the excitation distribution of a discrete array, and it is based on the Fourier Transform. Due to the finite nature of arrays, it is possible to use the Fast Fourier Transform, which permits an efficient and rapid way to perform the calculations using a computer. The method is applied to some typical synthesis problems and compared with published results. It can be seen that this new theory simplifies the usual synthesis procedure and that the processing time is dramatically reduced. Then, the method is applied to obtain synthesis techniques that control the radiation pattern through the use of the Fourier Transform properties.

Neural network-based array synthesis in presence of obstacles

Traditional antenna array synthesis methods are based on the reconstruction of the array factor coefficients or an equivalent current distribution over a surface enclosing the antenna. The coefficients are then used to estimate the amplitude and phase of the feeding voltages that must be applied to the ports of the array in order to generate a radiated field distribution according to a given set of specifications. These approaches consider implicitly that the radiating elements are ideal, without any interaction between them. Coupling effects between the individual radiating elements once inserted in the array might modify their radiation properties and the necessary feeding values for a specified radiated field distribution, affecting significantly to the global behaviour of the array. Neural networks (NN) offer an efficient way to incorporate the real radiating properties and the coupling effects between the elements of an array in the synthesis process without increasing the complexity of the model from the designer's point of view. Once the coupling effects are modelled, they can be used to perform synthesis tasks in presence of any obstacle in a near environment, which can be modelled as a part of the array, with the induced currents established by the coupling effects taken into account in the NN learning procedure. Copyright © 2005 John Wiley & Sons, Ltd.

Support vector regression for the design of array antennas

The support vector regression (SVR) framework is proposed as the basis for a new antenna array synthesis technique suitable to solve practical problems with high accuracy using a more appropriate cost function for template-based specification handling. Two design examples are presented to show the efficiency of the proposed method.

A quadratic‐programming approach to the synthesis of multifrequency broadside antenna arrays

AbstractAn array synthesis technique that straightforwardly extends an earlier quadratic‐programming based method to the case of multifrequency arrays is described. The method is applied to a specific multifrequency array example. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 43: 326–330, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20459

New method of the synthesis of multiband fractal antenna arrays based on a family of atomic functions

New method of the synthesis of multiband fractal antenna arrays based on a family of atomic functions

Perturbation of the phases of Taylor field samples in the synthesis of linear and circular array antennas

Antenna design specifications do not usually restrict the phase of the radiated field. Antenna synthesis techniques generally exploit this freedom only indirectly, but direct phase perturbation can be fruitful.