Design Of Linear Antenna Arrays Research Articles

Systematic approach to sidelobe reduction in linear antenna arrays through corporate‐feed‐controlled excitation

A systematic approach to sidelobe reduction of broadside linear antenna arrays by design of corporate feeds implementing non-uniform excitation is proposed. The approach comprises of the two major stages. First, candidate excitation sets (corporate feeds) are searched among excitation sets which might energize a particular array aperture. The range of the corporate networks is restricted – based on the implementation and performance considerations – to the ones constructed using only T-junctions with equal power split. The reference for the candidates in terms of the sidelobe level is the uniformly excited and spaced array aperture with the same number of elements. The search is performed over realizable excitations and constrained elements’ spacings using the array factor and the model of excitation amplitudes due to corporate feeding. At the second stage, an excitation providing reduced sidelobes and simple routing is implemented as a microstrip corporate feed within the antenna array model. The entire antenna-feed circuit is adjusted and validated using numerical optimization and discrete electromagnetic simulations at the high-fidelity level of description. The proposed feeds and the approach to their systematic design offer flexibility in design of linear antenna arrays without extra complications and allow substantial sidelobe reduction. A demonstration example is provided.

Hybrid Evolutionary Algorithm Genetical Swarm Optimization for 1D and 2D Annular Ring Unequally Spaced Antennas Arrays Synthesis

ABSTRACTIn this article, an innovative technique to design 1D and 2D annular ring unequally microstrip antenna arrays for the two modes TM11 and TM12 is introduced. The structure analysis is based on the modified cavity model, which allows to take into account the fringing fields effects along the ring-curved edges by employing dynamic permittivity. Design of linear and planar antenna arrays consider the optimization of the complex excitations values of the feeding network and the element locations first and a predefined magnitude’s Gaussian and uniform feed arrays of two modes second by using the genetical swarm optimizer method. The design of aperiodic linear antenna arrays is modeled as a single-objective problem, and the optimization of 2D aperiodic antenna arrays for both E and H planes and for both modes are also expanded to multi-objective cases. In terms of beam steering and low side-lobes level, the results presented in this article show interesting characteristics in the array factor response. Examples are given to illustrate the achievable performance.

Non-Uniform Linear Antenna Array Design and Optimization for Millimeter-Wave Communications

In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here the EMG is defined as the number of signal streams that are practically supported by the channel at a finite SNR. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared to the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that, the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-point distribution. Specifically, the antennas should be physically grouped into K separate uniform linear antenna arrays (ULAs) with the minimum feasible antenna spacing within each ULA, where K is the target EMG to be achieved; in addition, the centers of these K ULAs follow the Fekete-point distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch type (PAT) NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.

Synthesis of linear antenna array using flower pollination algorithm

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.

Design and Thinning of Linear and Planar Antenna Arrays Using a Binary Teaching Learning Optimizer

In this paper, a binary variant of the teaching learning optimization technique is used to the design and thinning of linear and planar arrays. The purpose of the optimization task is to enhance the ratio directivity/sidelobe level which turns out to be having two conflicting parameters. The binary variant of the teaching learning optimization technique searches the way of exciting some selected elements. The array thinning problem requires some elements to be excited with the others having no current in them. This is a binary (ON-OFF) problem that requires an optimization technique that can handle the binary variables. The teaching learning optimization has been proposed initially to handle real valued variables. The results show good agreement between the desired and calculated radiation patterns with reduction in resource usage in terms of power consumption.

Optimum design of non-uniform symmetrical linear antenna arrays using a novel modified invasive weeds optimization

Abstract This paper presents a new modified method for the synthesis of non-uniform linear antenna arrays. Based on the recently developed invasive weeds optimization technique (IWO), the modified invasive weeds optimization method (MIWO) uses the mutation process for the calculation of standard deviation (SD). Since the good choice of SD is particularly important in such algorithm, MIWO uses new values of this parameter to optimize the spacing between the array elements, which can improve the overall efficiency of the classical IWO method in terms of side lobe level (SLL) suppression and nulls control. Numerical examples are presented and compared to the existing array designs found in the literature, such as ant colony optimization (ACO), particle swarm optimization (PSO), and comprehensive learning PSO (CLPSO). Results show that MIWO method can be a good alternative in the design of non-uniform linear antenna array.

Dual-Band Linear Antenna Array for Harmonic Sensing Applications

This letter presents the design and demonstration of a single-port dual-band linear patch antenna array operating at frequencies that are multiples of the fundamental frequency (12 and 24 GHz, for example in this demonstration) for harmonic sensing applications. The proposed antenna consists of a low-band serial feed patch antenna array and two high-band patch antenna arrays that couple to the feeding circuit over the high frequency band. In the low frequency band, the feeding circuit does not couple with the high band arrays. Simulated and measured results are presented to validate the proposed technique. The low-pass characteristics are demonstrated numerically. The measured reflection coefficients are lower than $-\hbox{18 dB}$ at the operating frequencies and the measured gains are higher than 13 dBi.

Simulation-Based Design of Microstrip Linear Antenna Arrays Using Fast Radiation Response Surrogates

A fast yet accurate technique for simulation-based design of linear arrays of microstrip patch antennas is presented. Our technique includes: 1) optimization of the corrected array factor of the antenna array under design for a phase excitation taper resulting in reduced sidelobes; 2) simulation-driven optimization of the array element for element dimensions resulting in matching at and about operational frequency; and 3) simulation-driven optimization of the antenna array with controlling both radiation and reflection response. Models of different fidelity are utilized in the design process. The proposed technique produces optimized designs at the high-fidelity level of description. Operation and costs of our technique are demonstrated with design of a 10-GHz broadside linear array of slot-fed microstrip patch antennas.

Compressive sensing‐based approach to the design of linear robust sparse antenna arrays with physical size constraint

In sparse arrays, the randomness of antenna locations avoids the introduction of grating lobes, while allowing adjacent antenna spacings to be greater than half a wavelength. This means a larger array size can be implemented using a relatively small number of antennas. However, careful consideration has to be given to antenna locations to ensure that an acceptable performance level is achieved. Model perturbations can also cause steering vector errors, which in turn cause discrepancies in the array's response, making robust arrays desirable. This study presents various compressive sensing-based methods that can solve this problem, while also imposing the antenna size as a constraint on the minimum adjacent antenna separations. Narrowband and multiband design examples are presented to verify the effectiveness of the proposed design methods, with comparisons being drawn with a previously proposed genetic algorithm-based approach.

Design and Performance Comparison of Planar and Linear Antenna Array Using ADS

Design and Performance Comparison of Planar and Linear Antenna Array Using ADS

CRB-Based Design of Linear Antenna Arrays for Near-Field Source Localization

This paper is devoted to the Cramer Rao bound (CRB) on the angle and range of a narrow-band near-field source localized by means of an arbitrary linear array using the exact expression of the time delay parameter. First, we prove that the conditional and unconditional CRBs are proportional for an arbitrary parametrization of the steering vector (that may include, but is not limited to, the source DOA, range or polarization). Then, a Taylor expansion of the CRB is conducted to obtain accurate nonmatrix closed-form expressions of the CRB on angle and range. In contrast to the existing expressions, our expressions are simple, interpretable and more general because the sensors are only constrained to be placed along some axis. Our analysis leads to the characterization and design of a class of centro-symmetric linear arrays with improved near-field angle and range estimation capabilities.

Pattern Synthesis for Linear Antenna Array Using Characteristics Evolution Optimization

Linear antenna array design is one of the most important electromagnetic optimization problems of current research interests. In this paper a new method of optimization, Characteristics Evolution Optimization, has been introduced. The method is based on the binary digit system. Since the algorithm works on binary digits and in a parallel fashion, the method can perform well in parallel processing environment. For the purpose of illustration, a 16element array has been considered. The performance of the introduced technique has been compared with some of the existing techniques, IWO (Invasive Weed Optimization), DE (Differential Evolution), and PSO (Particle Swarm Optimization). Various variants of Invasive Weed Optimization have also been considered. It has been observed that the proposed method (Characteristics Evolution optimization) performs satisfactorily comparing with other existing methods. It has been observed that the introduced method, in certain instances, outperforms other optimization techniques.

Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms

In recent years Evolutionary computation has its growth to extent. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest. In this paper, the design of non-uniform linear antenna arrays with optimum side lobe level reduction is investigated. Two global evolutionary optimization methods are Particle Swarm Optimization algorithm(PSO) and Differential Evolution algorithm(DE ) are used to determine an optimum set of weights and positions that provide a radiation pattern with optimum side lobe level having uniform progressive phase between the elements. Simulation results show considerable enhancements in array performances using the global optimizers. The paper finally illustrates a comparative evaluation of the two proposed algorithms regarding their applicability as numerical optimization techniques.

ANALYSIS AND DESIGN OF MILLIMETER-WAVE CIRCULARLY POLARIZED SUBSTRATE INTEGRATED TRAVELLING-WAVE ANTENNAS

Circularly polarized millimeter-wave travelling-wave antennas, using substrate integrated circuits (SICs) technology, are designed, fabricated and tested. By using the SICs technology, compact antennas with low losses in the feeding structure and with good design accuracy are obtained. The elementary antenna which is composed of two inclined slots is characterized by full-wave simulations. This characterization is used for the design and development of linear antenna arrays with above 16dB gain and low side lobe level (< i25dB), using difierent power aperture distributions, namely uniform, Tchebychev and Taylor. Experimental results are presented at 77GHz showing that the proposed antennas present good performances in terms of impedance matching, gain and axial ratio. These antennas have potential applications in integrated transceivers for communication and radar systems at millimeter-wave frequencies.

DESIGN OF LINEAR AND CIRCULAR ANTENNA ARRAYS USING CUCKOO OPTIMIZATION ALGORITHM

Cuckoo optimization Algorithm (COA) is employed for the optimization of linear and non- uniform circular antenna arrays. COA is a novel nature inspired computing algorithm which is motivated by the life of Cuckoo. Like other nature-inspired algorithms, COA is also a population-based method and uses a population of solutions to proceed to the global solution. The method of COA is used to determine a set of parameters of antenna elements that provide the required radiation pattern. The efiectiveness of COA for the design of antenna arrays is shown by means of numerical results. Comparison of results of COA is made with that obtained using other popular methods. The results reveal the superior performance of COA as compared to other techniques both for design of linear and circular antenna arrays.

Antenna Array Synthesis and Failure Correction Using Differential Search Algorithm

Differential search (DS) optimization algorithm is proposed for the synthesis of three different types of linear antenna array design examples. The first group of examples is that DS algorithm is used to locate wide nulls on the linear antenna array patterns by controlling amplitude-only. In these examples, sidelobe levels disposed to rise are also suppressed by using DS algorithm in the same optimization process. In the second group of examples, individual nulls are placed with the help of DS algorithm by controlling the amplitude-only, phase-only, and position-only. The last example is a linear antenna array failure correction example. In order to tolerate the element failures, DS is employed to recalculate the amplitude values of the remaining intact elements of the antenna array. The results show that DS is very capable to solve the linear antenna array optimization problems which have different characteristics.

Design of Linear and Elliptical Antenna Arrays Using Biogeography Based Optimization

In this paper, the problem of designing linear and elliptical antenna arrays for specific radiation properties is dealt with. The biogeography-based optimization (BBO) method, which represents a new evolutionary algorithm, is used in the optimization process. BBO is used to minimize the maximum side lobe level (SLL) and null control for isotropic linear antenna arrays by optimizing different array parameters (position, amplitude, and phase). Similarly, for elliptical antenna array, four optimization techniques (BBO, genetic algorithm, self-adaptive differential evolution, and sequential quadratic programming) are used to determine an optimum set of weights that provide a radiation pattern with maximum SLL reduction with the constraint of a fixed major lobe beam width. The obtained results show the effectiveness of BBO compared to other well-known optimization methods.

Robust mask‐constrained linear array synthesis through an interval‐based particle SWARM optimisation

An innovative strategy for the robust design of linear antenna arrays is presented. Being the array elements characterised by tolerance errors, the synthesis is aimed at determining the intervals of values fitting the user-defined mask constraints on the radiated power pattern. With reference to the upper and lower bounds of the power pattern analytically determined for given tolerances through interval analysis, the nominal excitations of the array elements are then optimised by means of a global stochastic optimiser suitably customised to deal with interval numbers. A set of numerical examples is reported to show the behaviour of the proposed method as well as to assess its potentials in dealing with the robust synthesis of pencil and shaped beams.

Optimal synthesis of linear antenna array with composite differential evolution algorithm

Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently develop metaheuristic algorithm, known as the composite differential evolution (CoDE), to optimize a spacing between the elements of the linear element that provides a radiation pattern with minimum side lobe level and null placement in the specified direction. The CoDE has been used to solve three difficult instances of the design problem, and the optimization goal in each example is easily achieved. The experimental results of the CoDE algorithm have been shown to be better than the recently published results obtained using other state-of–the-art metaheuristics like DE, jDE, SaDE, JADE, BBO, GA, PSO and ES in a statistically meaningful way.

Nonuniformly Spaced Linear Antenna Array Design Using Firefly Algorithm

A nonuniformly spaced linear antenna array with broadside radiation characteristics is synthesized using firefly algorithm and particle swarm optimization. The objective of the work is to find the optimum spacing between the radiating antenna elements which will create a predefined arbitrary radiation pattern. The excitation amplitudes of all the antenna elements are assumed to be constant. The optimum spacing between the array elements are obtained using firefly algorithm. The minimum allowed distance between the antenna elements is defined in such a way that mutual coupling between the elements can be ignored. Numerical analysis is performed to calculate the far-field radiation characteristics of the array. Two numerical examples are shown to form two different desired predefined radiation patterns. The performance of the firefly algorithm and particle swarm optimization is compared in terms of convergence rate and global best solution achieved. The performances of the optimized nonuniformly spaced arrays are analyzed. Finally, contour plots of the radiated power from the optimized array in the horizontal plane and vertical plane in the far-field region are provided.