The present work studies the design of a high impedance surface (HIS)-based bowtie antenna in the framework of characteristic mode analysis (CMA) and proposes the method of higher order mode suppression. A triangular-elliptical bowtie antenna operating in the frequency range of 1.6–6 GHz is designed. The radiating and higher order modes of the proposed antenna are identified using CMA, and an HIS structure is used to enhance the desired mode and to suppress the higher order mode in order to get high gain, good front-to-back ratio (FBR), and stable radiation characteristics. The final designed HIS-based bowtie antenna gives stable radiation patterns from 1.7 to 5.5 GHz with a maximum boresight gain of 10.5 dB. Also, gain from 6.5 to 12 dB and FBR from 8 to 18 dB are obtained in the operating bandwidth. The proposed antenna features the advantages of low profile, wideband and high boresight gain making it suitable for ground-penetrating radar applications.

With the increasing demand for smarter antenna design in advanced technology applications, well-designed antennas have been an important factor in enhancing system performance. Most traditional antenna design requires multiple iterations and extensive testing to produce a final product. Machine learning (ML) algorithms have been used as an alternative to predict the optimal design parameters, but the outcome depends highly on the ML model efficiency. With recent development in machine learning algorithms and the availability of data for antenna design, we investigated different machine learning algorithms for optimizing the output strength of three basic antennae by analyzing the signal strength of the antenna for various antenna parameters. Different regression-based ML models were used to learn the behaviors and efficiency of three different antennas and to predict the output strength (S11) for different ranges of frequencies. The experiment compared and analyzed these ML regression algorithms for three different antennas: shot antenna, patch antenna, and bowtie antenna. In addition, the paper also provides comparison of ensemble ML models for performance analysis using the best three ML algorithms from the preliminary study. This study optimizes antenna parameters and quicker and smarter antenna design procedure using ML algorithms as compared to traditional design methods.