Suad Basbug

INTERFERENCE SUPPRESSION OF LINEAR ANTENNA ARRAYS BY AMPLITUDE-ONLY CONTROL USING A BACTERIAL FORAGING ALGORITHM

This paper presents a bacterial foraging algorithm (BFA) for null steering of linear antenna arrays by controlling only the element amplitudes. The BFA is a new evolutionary computing technique based on the foraging behavior of Escherichia (E.) coli bacteria in human intestine. To show the accuracy and flexibility of the proposed BFA, several examples of Chebyshev array pattern with the imposed single, multiple and broad nulls are given. It is found that the nulling method based on BFA is capable of steering the array nulls precisely to the undesired interference directions.

A PLANT GROWTH SIMULATION ALGORITHM FOR PATTERN NULLING OF LINEAR ANTENNA ARRAYS BY AMPLITUDE CONTROL

A method based on plant growth simulation algorithm (PGSA) is presented for pattern nulling by controlling only the element amplitudes of linear antenna array. The PGSA is a new and highly e-cient random search algorithm inspired by the growth process of plant phototropism. Simulation results for Chebyshev patterns with the imposed single, multiple and broad nulls are given to show the performance of the proposed method.

Backtracking Search Optimization Algorithm for Synthesis of Concentric Circular Antenna Arrays

A backtracking search optimization algorithm (BSA) is proposed for the synthesis of concentric circular antenna arrays (CCAAs) with the low sidelobe levels at a fixed beamwidth. Several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also given to illustrate the performance and flexibility of the proposed algorithm. BSA is a relatively new population based evolutionary optimization algorithm. The numerical results show that the design of CCAA using BSA provides good sidelobe levels with a fixed beamwidth. The nulling patterns obtained by BSA are also very good. The quality of results obtained by the BSA is checked by comparing with that of several metaheuristic algorithms in the literature. BSA is also compared with differential search (DS) and bacterial foraging algorithms (BFA) in terms of iterative performances.

Bacterial Foraging Algorithm for Null Synthesizing of Linear Antenna Arrays by Controlling Only the Element Positions

A method based on the bacterial foraging algorithm (BFA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling only the element positions. The BFA is a new evolutionary computing technique, based on the foraging behavior of Escherichia (E) coli bacteria in a human intestine. Simulation results for Chebyshev patterns with the imposed single, multiple and broad nulls are given to show the effectiveness of the proposed method. For practical consideration, due to small variations of the element positions, the sensitivity of the produced patterns is also examined by rounding the element position values to the second decimal position.

Null Synthesis of Time-Modulated Circular Antenna Arrays Using an Improved Differential Evolution Algorithm

An efficient variant of differential evolution (DE) algorithm, namely subpopulated differential evolution (subDE), is proposed for solving null synthesis problems of time-modulated circular antenna arrays by controlling time intervals and phases. The capabilities of subDE algorithm are examined by comparing its convergence curves and number of successful optimizations to those of the classical DE. Wilcoxon rank-sum test is also applied to ensure that the results are significant in terms of statistics. Furthermore, the antenna array parameters achieved by using subDE are compared to those of the classical DE in the literature. The results show that subDE can exhibit better performance than the classical DE for our antenna array synthesis examples. Additionally, the simulations are executed on an embedded microprocessor to investigate if subDE can also run on such a limited environment. The results demonstrate that the contemporary embedded systems hold promise for evolutionary algorithms.

Linear Antenna Array Synthesis Using Mean Variance Mapping Method

Abstract A mean variance mapping optimization algorithm for three types of antenna array synthesis examples is presented. Mean variance mapping optimization is based on the strategic transformation used for mutating the offspring built on mean variance of a certain dynamic population. The first set of examples is based on side-lobe suppression and null control by controlling element positions with mean variance mapping optimization under constraints to beam width. In the second group of examples, mean variance mapping optimization becomes an optimization tool to calculate the amplitude and phase values of antenna array elements to synthesize shaped beams. In the last set of examples, mean variance mapping optimization is used to mitigate the effect of failure elements on the pattern by recalculating the amplitude values of remaining elements. Additionally, a modification that provides a good balance between exploitation and exploration is applied to the mean variance mapping optimization algorithm to improve its stability and accuracy. The simulation results showed that the proposed mean variance mapping optimization is an effective and easy implementable technique for different antenna array synthesis problems.

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.

A general model for pattern synthesis of linear antenna arrays by metaheuristic algorithms

In this paper, a general model is proposed for the systems that optimize the linear antenna array parameters to synthesize a desired pattern by using metaheuristic algorithms. The model consists of a masking mechanism for the pattern shaping and a modular system for the whole optimization structure. To validate the skills of the proposed model, an antenna array synthesizer software is developed and tested with six different pattern examples. The results show that this implementation based on the proposed method can achieve the desired patterns successfully. The source code and compiled version of the software are shared on the Internet.

Design and Synthesis of Antenna Array With Movable Elements Along Semicircular Paths

A reconfigurable antenna array design whose array elements can move along a semicircular path to synthesis radiation patterns is proposed. This movement is simply carried out by stepper motors without using any other actuators. The array factor of the new design is derived in order to construct the objective function and solve the optimization problem of required pattern synthesis. To validate the design, array patterns with single, symmetric double, multiple, and wide nulls are computed by only controlling element rotation angles with a quantized differential evolution algorithm. The results show that the obtained patterns fit in the restriction masks successfully. The method presented in this letter can also provide both symmetric and asymmetric patterns since the array elements have a freedom of movement in two dimensions.