Antenna Design and Direction of Arrival Estimation in Meta-Heuristic Paradigm: A Review

Antenna Design and Direction of Arrival Estimation in Meta-Heuristic Paradigm: A Review

Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India) and Amira S. Ashour (Department of Electronics & Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt)
DOI: 10.4018/IJSSMET.2016070101
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Antennas are considered as a significant component in any wireless system. There are numerous factors and constraints that affect its design. Therefore, recently several algorithms are developed to allow the designers optimize the antenna with respect to numerous different criteria, general constraints and the desired performance characteristics. In recent years there has been an increasing attention to some novel evolutionary techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria-Foraging (BF), Biogeography Based Optimization (BBO), and Differential Evolution (DE) that used for antenna optimization. The current study discussed three popular population-based meta-heuristic algorithms for optimal antenna design and direction of arrival estimation. Basically, single and multi-objective population-based meta-heuristic algorithms are included. Besides hybrid methods are highlighted. This paper reviews antenna array design optimization as well as direction of arrival optimization problem for different antennas configurations.
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1. Introduction

Antenna is an electrical device that forms an interface between free space radiations and transmitter or receiver. There are several antenna types such as Omni antennas, High-gain antennas, Reconfigurable antennas, planar antennas, Reflector antennas, Planar UWB antennas, and Patch antennas. Antenna arrays provide an efficient means to detect and process signals arriving from different directions. Several critical parameters affecting an antenna's performance and design, such as resonant frequency, side lobes levels minimization, impedance, gain, aperture or radiation pattern, polarization, efficiency and bandwidth. Since, antenna design is usually depends on the designers’ experience, which is tedious and time-consuming. Consequently, this leads to huge difficulty for the designer to modify the structure in order to compromise between these parameters. Similarly, different criteria are required for direction of arrival (DOA) estimation problems. Recently, the researchers develop optimization algorithms to solve these difficulties.

Generally, optimization is the maximization or minimization of any function relative to some set that representing a range of choices available in a certain situation. This function allows comparison of different choices to determine the “best”. Therefore, antenna optimization methods have been presented using objective function (OF) or named fitness (cost) function. In the current study the antenna optimization methods are studied for to aspect:

  • 1.

    Optimization for the antenna design,

  • 2.

    Optimization for direction of arrival estimation (DOA).

Antenna design optimization aims at create superior and complex antenna structure that is competitive in terms of performance and cost effectiveness. In addition to, optimization for DOA estimation is used to avoid trapping in local minima while determining the exact signal direction and suppress interference. The local minima may occur during target tracking based DOA algorithms.

The optimization process involves selection of fitting objective functions, design variables, parameters, and constraints. In the majority of antenna optimization problems, several goals must be satisfied simultaneously to obtain an optimal solution. In many situations, antenna optimization can be viewed as a multidisciplinary problem. The objective function (OF) may have several local minima which leads excessive finding to global minimum. Therefore, using an appropriate method for the given problem is fundamental for a proper engineering solution.

Since, at several conditions the deterministic methods work improperly, thus stochastic methods become a good alternative. Actually, stochastic methods such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS) (Dey et al., 2013) and Firefly Algorithms (FA) (Dey et al., 2014) are known to be very robust and capable of finding good results. These evolutionary algorithms (EA) are extensively used to optimize various electromagnetic devices (Griffiths et al., 2006; Perez & Basterrechea, 2007; Karimkashi & Kishk, 2010). Over years evolutionary optimization strategies are highly efficient tools to solve problems when it impossible to use an exhaustive search approaches. The PSO for example, is inspired by the social behavior of swarms of fish, birds, or insects. It is a bio-inspired technique that developed by Kennedy and Eberhart (1995) to perform an intelligent search in continuous and multidimensional solution spaces. The PSO is a relatively new approach to Electro Magnetic (EM) optimization and design (Robinson & Rahmat-Samii, 2004). As well as the Genetic Algorithm which has become popular and was introduced to the computational electromagnetic as efficient global optimization tool in their design and is presently used in a wide variety of electromagnetic applications (Johnson & Rahmat-Samii, 1999). Genetic Algorithm based optimization technique utilizes the recursive nature in arriving at an optimal solution from randomly generated various possibilities.

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