Optimization of array pattern for efficient control of adaptive nulling and side lobe level

Abstract

The array synthesis problem is modeled as a nonlinear optimization problem with the constraints of a reduced side lobe level (SLL). The goal is to impose deeper nulls in the interference direction of an uniform linear antenna arrays. This paper focuses on the array synthesis of adaptive nulling and side lobe level control which is a highly nonlinear problem fit for an evolutionary optimization algorithm. The limitations with the available optimization algorithms are mainly subjected to premature convergence and stagnation problem hence resulting in a non satisfactory array synthesis results. Hence, a new optimization technique known as Collective Social Behavior (CSB) is developed with concepts adopted from evolution of social behavior to address the current limitations to achieve a better result in array synthesis problem. The optimal set of weight coefficients for the linear antenna arrays is determined by the CSB algorithm by evaluating the objective function for the array synthesis problem. To establish the CSB algorithm as an efficient optimization tool for adaptive nulling applications, several numerical benchmark tests have been conducted. The simulation results reveal that the proposed CSB algorithm enhances the performance of array pattern synthesis with precise deeper nulls and suppressed side lobe levels.

Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm

“…In [14], Saxena et al introduced flower pollination algorithm (FPA) for minimizing SLL and placing deep nulls in specific directions by optimizing antenna positions in linear antenna arrays. Sankar et al [15] developed collective social behavior (CSB) technique for SLL control and adaptive nulling by optimizing antenna weights.…”

Multi-objective concurrent optimization of antenna array parameters for 5G applications

“…To simultaneously improve the conflicting characteristics, a multiobjective optimization of antenna array parameters is to be implemented. Significant work is also carried out in this area [21][22][23][24][25][26][27][28][29][30][31][32]. In [21], two design objectives -SLL and null control in specified directions are minimized simultaneously by controlling spacing between array elements using differential evolution.…”

“…One more conflicting pairfixed interference suppression and avoiding further rise of SLL, while scanning the beam, are simultaneously achieved by using GA in [22]. Collective social behavior (CSB) algorithm is developed in [23] to minimize SLL and achieve adaptive nulling using antenna weight controls.…”