PATTERN SYNTHESIS OF NON-UNIFORM AMPLITUDE EQUALLY SPACED MICROSTRIP ARRAY ANTENNA USING GA, PSO AND DE ALGORITHMS (original) (raw)
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Antenna array systems with low side lobe levels are essential for today wireless communication systems. This paper presents the synthesis of unequally spaced linear rectangular micro strip antenna array with minimum side lobe levels using the novel evolutionary algorithm known as improved local search particle swarm optimization (ILSPSO). ILSPSO is a modified version of particle swarm optimization (PSO), in which Gaussian distribution is used to enhance the local search of the PSO. In this paper, ILPSO is applied to optimize the positions of the micro strip antenna elements to suppress the peak side lobe level (PSLL) along with PSO and differential evolution (DE) algorithms. The steps involved in problem formulation along with design examples illustrating the performance of the ILPSO in minimizing the side lobe levels are demonstrated. A 20 and 32 element linear micro strip rectangular patch antenna (MSRPA) element are considered to show the effectiveness of the proposed method. The optimized micro strip antenna array is simulated using high frequency structure simulator (HFSS). The synthesis results demonstrate that the ILSPSO outperforms PSO and DE in terms of producing lower PSLL and convergence rate. The flexibility and ease of implementation of the ILSPSO algorithm is obvious from this paper, showing the algorithms usefulness in other array synthesis problems.
Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms
IOSR Journal of Electronics and Communication Engineering, 2014
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.
DESIGN OF NON-UNIFORM CIRCULAR ANTENNA ARRAYS USING PARTICLE SWARM OPTIMIZATION
2008
In this paper, the design of non-uniform circular antenna arrays with optimum side lobe level reduction is presented. The particle swarm optimization (PSO) method, which represents a new approach for optimization problems in electromagnetics, is used in the optimization process. The method of particle swarm optimization is used to determine an optimum set of weights and antenna element separations that provide a radiation pattern with maximum side lobe level reduction with the constraint of a fixed major lobe beamwidth. The results show that the design of non-uniform circular antenna arrays using PSO method provides a side lobe level reduction better than that obtained using genetic algorithms.
Array Antenna Pattern Synthesis using Improved Particle Swarm Optimization (IPSO) Algorithm
ECTI Transactions on Electrical Engineering, Electronics, and Communications
Antenna arrays are used in many different systems, including radar, military systems, and wireless communications. The design of the antenna array has a significant impact on how well the communication system performs. The large number of pieces and the large sidelobe levels provide the biggest design hurdles for such arrays. The antenna arrays have recently been heavily thinned using optimization approaches that take advantage of evolutionary algorithms in order to lower power consumption and enhance the radiation pattern by lowering sidelobe levels. A global optimum for this kind of algorithm is not guaranteed, though, because of the stochastic nature of the resolution techniques. This work characterizes the optimal pattern synthesis of a linear array antenna using the Improved Particle Swarm Optimization (IPSO) algorithm. The main aim is to obtain a low Side Lobe Level (SLL) that avoids interference and a narrow beam width for acquiring high directivity to obtain the optimal solu...
2008
In this paper, a technique based on hybrid particle swarm optimiser with breeding and subpopulation is presented for optimal design of reconfigurable dual-beam linear array antennas and planar arrays. In the amplitudephase synthesis, the design of a reconfigurable dual-pattern antenna array is based on finding a common amplitude distribution that can generate either a pencil or sector beam power pattern, when the phase distribution of the array is modified appropriately. The goal of this study is to introduce the hybrid model to the electromagnetic community and demonstrate its great potential in electromagnetic optimizations.
Linear Antenna Array Synthesis Using Improved Particle Swarm Optimization
International Conference on Emerging Applications of Information Technology, 2011
In this paper the synthesis of linear array geometry with minimum sidelobe level using a new class of Particle Swarm Optimization technique namely Improved Particle Swarm Optimization (IPSO) is described. The IPSO algorithm is a newly proposed, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and
Side Lobe Level Optimisation of Circular Microstrip Array Antenna Using Genetic Algorithm
TJPRC, 2014
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. The popularity of micro strip antennas are increasing day by day because of ease of analysis and fabrication, and their attractive radiation characteristics. So the micro strip Antenna are very useful & essential device for effective wireless communication. This paper focuses on the application of binary coded Genetic Algorithm (BGA) which is applied to the Circular Patch Microstrip antenna with linear and non linear (Dolph-Chebyshev) arrays. The cost function of Genetic Algorithm(GA) is maximum reduction in side lobe level of the radiation pattern of the antenna .The genetic algorithm finds the optimum amplitude current excitations co-efficient of the antenna array elements to provide the radiation pattern with maximum reduction in the side lobe level and also achieved the minimum possible null to null beam width, the resultant radiation patterns for both before GA and after GA of Microstrip array are compared. The Radiation patterns are presented for different number of elements. All the simulated results are obtained by using MATLAB software.
Design of Non-Uniform Antenna Arrays Using Genetic Algorithm
Journal of Wireless Networking and Communications, 2012
The performance of a single-element antenna is somewhat limited. To obtain high directivity, narrow beamwidth, low side-lobes, point-to-point and preferred-coverage pattern characteristics, etc., antenna arrays are used. An antenna array is an assembly of individual radiating antennas in an electrical and geometrical configuration. Nowadays, antenna arrays appear in wireless terminals and smart antennas, so robust and efficient array design is increasingly becoming necessary. In antenna array design, it is frequently desirable to achieve both a narrow beamwidth and a low side-lobe level. In linear antenna arrays, a uniform array yields the smallest beamwidth and hence the highest directivity. It is followed, in order, by the Dolph-Chebyshev and Binomial arrays. In contrast, Binomial arrays usually possess the smallest side-lobes followed, in order, by the Dolph-Chebyshev and uniform arrays.Binomial and Dolph-Chebyshev arrays are typical examples of non-uniform arrays. In this paper we deal only with linear arrays and it is shown that using genetic algorithm it is possible to design a non-uniform array that approximates the beamwidth of a uniform array and having smaller side-lobe level than the Dolph-Chebyshev array. The result is that the designed antenna array exhibits the largest directivity as compared to the uniform, Binomial and Dolph-Chebyshev arrays. In the design, the genetic algorithm is employed to generate the excitation amplitudes of the antenna array.
Minimization of Side Lobe Level for Linear Antenna Arrays Using Improved Particle Swarm Optimization
2017
The present paper describes minimization of side lobe levels of an linear array antenna using strong evolution algorithm is of particle swarm optimization (PSO). For linear, non linear optimizations and to solve general dimensional problems with high performance a newly discovered PSO used. The implementation and mathematical preprocessing of PSO is easy to analyze when compared with other evolutionary algorithms like simulated annealing and genetic algorithm. The synthesis is based on minimization of side lobe levels in required directions with optimum amplitude distributions for properly arranged antenna array using PSO. Two design examples are considered with even and odd number of elements for the synthesis with required goal.