Radiation Patterns from Thinned and Unthinned Linear Arrays with Different Spacings Using ML Algorithms

Abstract

Antenna array thinning is the tuning of same antenna elements with uniform spacing or periodic antenna array to generate the desired amplitude density across the aperture area. Large antenna arrays are difficult to build and have increased fabrication cost. The process of eliminating the radiating elements from the array would be desirable if arrays performance is not significantly degraded. One method of achieving this goal is array thinning by systematically removing elements without change in the performance. This chapter presents and explores machine learning and its applications in the design of antenna array. This chapter also gives the characteristics of machine learning, deep learning, different learning algorithms and its usage in the design of an antenna array with thinning. This chapter presents the performance of an array with and without thinning and the radiation characteristics are observed for both the cases with different spacings. The major advantage of the present work is the reduction of number of elements to achieve better and specified Radiation patterns.