K.R. Subhashini

Bio: K.R. Subhashini is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Antenna array & Genetic algorithm. The author has an hindex of 2, co-authored 3 publications receiving 6 citations.

Reconfigurable array antennas with side lobe level control by genetic modulation

Abstract: In this paper the design problem of an equi-spaced linear array is considered with the constraint of reducing side lobe level which results in the leakage of the received signal energy. In order to achieve this, powerful tool based on the event of probability has to be chosen. We consider an evolutionary approach, which avoids the adaptive nature and avails the tuning mechanism. Genetic Algorithm which follows the nature dependent rules is used to achieve this purpose. The process of varying the characteristic parameter in the genetic optimization is termed as genetic modulation. In this paper the parameter of importance, is the mutation rate. The effectiveness of this variation, rule the design of linear array is shown by simulation results.

Genetic and TABU algorithm based array synthesis for the generation of a shaped beam antenna

Abstract: Antenna designers are constantly challenged with the temptation to search for optimum solutions for the design of complex electromagnetic devices. The ability of using numerical methods to accurately and efficiently characterizing the relative quality of a particular design has excited the engineers to apply stochastic global evolutionary optimizers for this objective. Evolutionary techniques have been applied with growing applications to the design of complex electromagnetic systems. These schemes are finding popularity in electromagnetic as design tools and problem solvers because of their flexibility, veracity and ability to optimize in complex multimodal search spaces. This paper discusses the design and optimization of antenna arrays with special emphasis on Genetic Algorithm and TABU search method. It also illustrates the implementation procedure of Genetic algorithm and Tabu search algorithm in electromagnetic optimization problems. The optimization procedure is then used to design linear antenna array with specific array factor requirements. A comparative study of both design tools is done through simulation.

Estimation of QPSK signal via different smart array antennas deploying gradient approach

Abstract: Smart Antenna systems have recently received increasing interest as the demand for better quality and new value added services on the existing wireless communication systems. These system of antennas include different array geometry and different adaptive techniques to enhance the received modulated signal with a fixed DOA by suppressing the inferences. Although enormous study has been done on smart antennas, special emphasis has not been provided to the antenna array structure. In this paper the performance of smart antenna in terms of the received signal for linear, circular and planararrays is observed. The adaptive array structure for all the geometry's is trained using modified LMS algorithm and RLS algorithm. Comparison is made for different array structures using both the adaptive techniques.

Introduction to Genetic Algorithms in Electromagnetics

Abstract: This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are "global" numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given. >

Linear Antenna Array Synthesis to Reduce the Interference in the Side Lobe Using Continuous Genetic Algorithm

Abstract: Sidelobes in the radiation pattern causes degradation of actual signal and hence antenna power efficiency. This paper discusses the problem of interference suppression by reducing the side lobe. Synthesis of linear antenna array using Continuous Genetic Algorithm is done to search for the optimum amplitude weights to minimize the maximum side lobe level. It has been demonstrated that side lobe level is reduced effectively by this technique. The simulated results shows an improved directivity, reduction in the first side lobe and fast convergence. This shows the effectiveness of this technique.

Side Lobe Level Reduction in Antenna Array Using Evolutionary Algorithms: A Review

Abstract: Antenna array design is one of the most imperative electromagnetic optimization problem of current interest. In the antenna arrays the side lobe level is main problem which causes waste of energy. In this paper, different evolutionary algorithms are presented for reduction of side lobe level in antenna array. The invasive weed optimization(IWO) algorithm outperform PSO, ACO, and GA based on metrics such as average final accuracy,side lobe level (SLL),directivity, convergence speed, and robustness.

The study in fast calculation of Numerical Green Matrix of a surface antenna in a plane

Abstract: In this paper, it is discovered that the correlation matrices of antennas whose scattering surface are within the same plane are diagonal matrices. By applying this conclusion to the Numerical Green Matrix Module in the NEC (Numerical Electromagnetic Code), the memory space used by simulation program is significantly reduced, and the computational speed is improved, when the initial structures are face structures within the same plane. In a simulated experiment of NASA ST5 antenna designing, the speed of computation with the method introduced in this paper is 25.3 times faster than that of original NEC, and uses as 1/858 memory as original NEC. Applying the approach to evolutionary antenna research can help with solving one of its great challenges: expensive and time-consuming computation.

Reducing the number of elements in a pattern reconfigurable antenna array by the multi-task learning

Abstract: The synthesis of pattern reconfigurable antenna array with as few elements as possible can find wide applications in radar tracking, biomedical imaging, satellite and ground communications, and remote sensing applications. In this study, an efficient method for multi-task learning (MTL) is exploited to the design of sparse pattern reconfigurable antenna array. Toward this end, the design of sparse and pattern reconfigurable antenna array is reformulated as an equivalent problem of multi-matrices linear regression and iterative shrinkage threshold method for MTL is utilised to obtain jointly optimal design of positions and the excitations of the radiating elements for multi-pattern synthesis, in which compromise between the array sparseness and pattern matching is achieved. Some numerical simulations are presented to assess the efficiency of the proposed method and the synthesis performance comparisons with matrix pencil method and genetic algorithm are also performed to demonstrate the superiority of the proposed algorithm over traditional algorithm in this literature.