Synthesis Of Linear Arrays Research Articles

Linear Antenna Array Synthesis by Modified Seagull Optimization Algorithm

This paper presents a study of linear antenna array (LAA) synthesis with a seagull optimization algorithm (SOA) to achieve radiation patterns having low maximum sidelobe levels (MSLs) with and without nulls. The SOA is a new optimization technique based on the moving and attacking behaviors of the seagull in the nature. In this study, the original mathematical model of SOA is modified by compensating the exploration and exploitation features to improve the optimization performance. The optimization ability of the modified SOA (MSOA) is tested with seven numerical examples of LAA. In the first three examples, the amplitude values of the array elements are optimized by MSOA whereas the element position values are calculated by MSOA for the last four examples. The numerical results obtained by MSOA are compared with those of different algorithms from the literature. The results reveal that MSOA algorithm is very good at optimizing antenna array parameters to obtain a desired radiation pattern. Additionally, it is seen that MSOA finds better results than the compared algorithms in terms of MSL and single and multiple null depth levels (NDLs). The contribution of the modification of MSOA is shown with a convergence curve to compare with the original one.

Null-Steering Beamformers for Suppressing Unknown Direction Interferences in Sidelobes

This paper proposes an efficient null-steering beamformer for adaptive pattern synthesis of uniform linear arrays based on binary bat algorithm (BBA) and the amplitude-only control of array excitation weights. The proposed beamformer is able to suppress unknown direction interferences in the sidelobes while simultaneously maintaining the main lobe and suppressing the sidelobes. The performance of the proposal has been investigated via a couple of scenarios including operation speed and adaptive null-steering ability with or without the effect of mutual coupling in half-wave dipole uniform linear arrays (DULA). The simulation results have proved that the proposed beamformer is a promising approach for adaptive pattern synthesis in the case of interference suppression. In addition, this proposal outperforms those using binary particle swarm optimization (BPSO) on the operation speed and null-steering efficiency.

Synthesis for sidelobe suppression of linear array based on improved grasshopper optimization algorithm with adaptive chaotic strategy

In view of the shortcomings of grasshopper optimization algorithm(GOA), such as insufficient exploration ability in the early stage and insufficient development ability in the later stage, this paper proposes a new adaptive chaotic GOA (ACGOA) by using curve adaptive adjustment strategy to replace the existing linear adaptive strategy, and using uniform chaos optimization to update the grasshopper position, so as to further improve the comprehensive ability of grasshopper swarms in the early exploration and later development, and better realize the balance between global search and local optimization ability. The effectiveness and stability of ACGOA in optimization calculation are tested by using a variety of single/multi peak benchmark function to get the extremum, and then applied to pattern synthesis of linear array for the first time, the target optimization performance of ACGOA is verified by suppressing the maximum SLL of linear array. The results show that compared with other existing algorithms, the algorithm has lower maximum sidelobe level and narrower first null beam width. The ACGOA results and convergence test show that the algorithm has superior applicability in antenna optimization.

Synthesis of sparse linear arrays using reweighted gridless compressed sensing

Pattern synthesis of the sparse linear array (SLA) has played an important role when the antenna size is extremely limited. Although grid-based compressed sensing (CS) algorithms have been widely utilised to synthesise the SLA, the performance is greatly affected by the grid mismatch problem. To solve the problem, a reweighted gridless CS (RGCS) algorithm based on the reweighted atomic norm minimisation and the rotational invariance propagator method is introduced. In the RGCS algorithm, the number of antenna elements can be efficiently reduced through the reweighted gridless convex optimisation, which utilises a reweighted strategy to break the limit of the atomic norm and improves the performance of the SLA. More importantly, in addition to the focussed-beam pattern, the proposed algorithm can also synthesise the SLA with the asymmetric beam pattern. Numerical experiments show that the RGCS algorithm can save about 18.75%–46% array elements for the uniform linear array.

Multi-population and dynamic-iterative cuckoo search algorithm for linear antenna array synthesis

Nature inspired algorithms have served as the backbone of modern computing technology and over the past three decades, the field has grown enormously. CS is a recent addition to optimization computing and suffers from problem of local optima stagnation and poor exploration. This paper presents a new version of CS namely extended CS (ECS) by employing the concept of multi-population adaptation, adaptive switching, dynamic iterative search, and GWO inspired global search phase, for solving benchmark problems and real-world design problem of linear antenna array (LAA) optimization. The algorithm aims at providing a better framework for optimization problems by keeping the original structure of CS intact. For performance evaluation, the algorithm has been firstly applied on two highly challenging datasets namely CEC 2015 and CEC 2017 benchmark problems and performance evaluation is done in comparison with other algorithms. Further to test the ECS algorithm on real world application, it is applied for synthesis of uniform and non-uniform LAA. Experimentally, the ECS algorithm is found to provide better performance in comparison to basic CS and others state-of-the-art algorithms. Statistical tests and radiation patterns further validate the results.

Synthesis of Linear Antenna Arrays using the Differential Evolution Algorithm with Current-Only Adaptive Nulling Technique

This paper presents an efficient method for the pattern synthesis of linear antenna array based on the differential evolution algorithm. A number of examples are presented to demonstrate the various capabilities of the this method for different number of elements. The main objective of this paper is to achieve a set of current weights in order to steer the nulls in undesired directions (nulling the undesired signals) and steering the main beam towards the desired direction(direction of desired signal) by changing the phase excitation currents. The fitness function is obtained from maximization of error between actual pattern and desired pattern.

Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers

Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.

Design of array antennas via atom search optimization

This paper introduces an advanced meta-heuristic optimization technique, namely, Atom Search Optimization (ASO) for the synthesis of linear and planar arrays. Synthesis of array antenna is a traditional electromagnetic problem. Investigations have been made by many researchers on synthesis of various antennas by applying several algorithms. ASO is a global optimization algorithm based on atom dynamics in dealing with different challenging problems. The ASO is applied to the array synthesis of the symmetric and asymmetric linear array antennas. To verify the effectiveness of the ASO algorithm, a comparative analysis of simulation results and published results for linear arrays has been made. The ASO optimized non uniform amplitude coefficients have been applied to the ON elements of the two ring concentric circular array. A comparative analysis with Firefly algorithm (FA) and Flower Pollination Algorithm (FPA) show the proposed algorithm is a comprehensive optimization algorithm in antenna array pattern synthesis.

Fast Synthesis Method for Large Aperture Array Pattern in the Presence of Array Errors

In this paper, an improved array synthesis method with array errors is proposed for large aperture arrays. Because of the array errors such as amplitude-phase errors and positions errors, the performance of the array synthesis is reduced seriously. Firstly, the ideal fast low sidelobe synthesis method is obtained based on the discrete Fourier transform (DFT) method. Then, by using Taylor expansion to remove the coupling relationship between the position of the element and the scanning angle, the compensation matrix for the pattern function and the array weighted vector with amplitude phase and position errors are derived. At last, the conversion relationship between the array with errors and the array weight vector is corrected in the iterative process. The theoretical simulation experiments verify the effectiveness and robustness of the proposed method for the linear array and rectangular array pattern synthesis. Then, the influence of Taylor expansion order on the pattern synthesis results is analysed.

A comparison of swarm-based optimization algorithms in linear antenna array synthesis

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with a low side lobe level (SLL) at a desired half power beam width in far-field. The amplitude and position values of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper, swarm-based metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10-, 16-, 24- and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers, and statistical results show that the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

A Dynamic Invasive Weeds Optimization Applied to Null Control of Linear Antenna Arrays with Constrained DRR

In the present work, a dynamic stochastic method is proposed and used for the synthesis of uniform linear antenna arrays. The proposed method combines the classical invasive weeds (IWO) and the mutation process, which makes it robust, simple and shows flexibility to be adapted. The dynamic IWO applies the mutation process in the calculation of standard deviation during the spatial dispersal process of produced seeds while keeping the mean at the parent plants. In the mutation process, if special conditions were achieved, the standard deviation would be re-initialized. This proposed method tries to achieve an optimal array pattern by acting on the relative amplitude excitation of each element in the linear array for an optimal inter-element spacing. The optimal array pattern has deep or broad nulls in some directions of interferences with low sidelobes level. The objective of the synthesis is to get amplitude excitations with low dynamic range ratio (DRR), which facilitates the design of beamforming feed network. To illustrate the robustness of the proposed method, numerical examples are presented and compared with the obtained results using bees algorithm (BA), bacterial foraging algorithm (BFA), real genetic algorithm (RGA), and the corresponding reference array pattern for each example.

Optimization of linear arrays using modified social group optimization algorithm

In this paper, optimization of the linear array (LA) antenna is performed using modified social group optimization algorithm (SGOA). First step of the work involves in transforming the electromagnetic engineering problem to an optimization problem which is completely described in terms of objectives. Linear array synthesis is inherently considered as a multi-attribute problem. The pattern synthesis of LA is carried out with several objectives involving sidelobe level (SLL), beam-width (BW) and desired nulls. The SLL suppression with BW constraint is considered as first objective of this work and the results are compared with several evolutionary computing algorithms like ant lion (ALO), grey wolf (GWO) and root-runner (RRA). Following this, the MSGOA is further used to synthesise null patterns in which the pattern is completely described in terms of nulls with SLL and BW as constraints. The entire simulation-based experimentation is performed using Matlab® on i5 computing system.

Gaussian Approach for the Synthesis of Periodic and Aperiodic Antenna Arrays: Method Review and Design Guidelines.

This paper presents a complete overview of the recently developed Gaussian approach for the synthesis of both periodic and aperiodic linear antenna arrays in conjunction with an exhaustive numerical investigation of the achievable performance. The position and excitation synthesis problems are jointly modeled to enable a direct mutual comparison between the two strategies. To this aim, different parameter settings are selected to analyze the results in terms of achieved beamwidth and maximum sidelobe levels as a function of the array aperture and of the number of radiating elements. The insights inferred from this numerical investigation are exploited to derive a novel first-step procedure with the purpose of enabling an antenna engineer to quickly identify the most suitable design approach, thus reducing the time required for antenna system development.

Linear Array Synthesis for Wireless Power Transmission Based on Brain Storm Optimization Algorithm

Based on the brain storm optimization algorithm, this paper proposed a new method to optimize the beam collection efficiency of the linear antenna array. In the process of optimization, constraints such as aperture size and minimum antenna spacing are considered. In this paper, the optimization with different antenna apertures, different antenna angles, and different array numbers are studied. A series of representative data and simulation results are given and the superiority of the brainstorming algorithm is demonstrated by comparing with the genetic algorithm.

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

Linear virtual antenna array synthesis method with angular superresolution of weak signals

Linear virtual antenna array synthesis method with angular superresolution of weak signals

Multi-Verse Optimization algorithm for the Optimal Synthesis of Phase-only reconfigurable linear array of mutually coupled parallel half-wavelength dipole antennas placed at finite distances from the ground plane

Researchers on antenna arrays usually neglect the effect of mutual coupling of antennas placed in proximity to each other. The interchange of electromagnetic energy happening between an antenna and a far field point depends not only on the transmitting antenna, but also from its neighbouring antennas. This effect is referred to as mutual coupling between dipole antenna elements and it is considered here in the synthesis of phase only reconfigurable antenna arrays. The main objective of this work is to produce desired Side Lobe Level and Voltage Standing Wave Ratio in addition to few other radiation pattern parameters. Multiverse Optimization algorithm is employed for the purpose of generating voltage amplitude and discrete phase distributions in the dipole elements for the generation of flat-top beam/pencil beam patterns. These two patterns share the common amplitude distributions and differ in phase distributions. Results obtained using MATLAB simulations prove that this algorithm accomplished its task successfully and is also found to be superior over other algorithms like Particle Swarm Optimization, Grey Wolf Optimization and Imperialist Competitive Optimization algorithms.

Optimal Pattern Synthesis of Linear Antenna Arrays Using Modified Grey Wolf Optimization Algorithm

The aim of this work is to show the effectiveness of a new algorithm named as modified grey wolf optimization (MGWO) algorithm to determine the optimum combination parameters values of a linear antenna array which is widely used in the communication systems. The selection part of the classical GWO has been modified by adopting the competitive exclusion selection inspired from genetic algorithm. The objective to be attained is a directional array factor with a very low level of lateral lobs. To this effect, a Gaussian function centered at 90° with the total absence of secondary lobs is considered as a desired diagram in our simulation. To matches the desired pattern as closely as possible, we considered the optimization of interspacing elements, weights amplitude and phase excitation of the linear antenna array factor. It has been demonstrated that the performance of a printed linear antenna array depends on all parameters, in which simultaneous optimization is imperative to maximize its characteristics. The obtained results show the effectiveness and the flexibility of the proposed algorithm in terms of minimized lateral lobe level compared to PSO algorithm and the convergence speed towards the desired solution.

Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

A new optimization algorithm applied in electromagnetics — Maxwell’s equations derived optimization (MEDO)

In this paper, a novel global optimization algorithm, named as Maxwell’s equations derived optimization (MEDO), is proposed. Using the Maxwell’s equations to analyse the behaviors of the time-varying current, the Ampere force is obtained from Fleming’s left hand rule. MEDO introduces an ‘Ampere force’ term, which is derived from Maxwell’s equations and is rigorous in physics, to drive the variables to the global optimal solution in the search space. In addition, introducing ‘gravity’ to MEDO can increase the stability of the optimizations. 11 classical benchmarks are tested, and results show that MEDO can always converge to numerical optimal solutions. To evaluate the proposed MEDO in solving the electromagnetic problems, four practical engineering applications are considered including the linear antenna array synthesis, frequency selected surface optimization, numerical dispersion reduction for finite-difference method, and parameters extraction of typical waveform. These examples are significant in electromagnetics, but tough to be solved because of their high dimensionality and strong nonlinearity. Numerical results show that MEDO can outperform several classic optimization methods, like wind driven optimization (WDO) and particle swarm optimization (PSO). Therefore, the electromagnetics-inspired MEDO is robust and of great potential in solving the electromagnetic optimization problems.