Design of Optimized low-power GPS-Yagi Antenna using Machine Learning techniques

The time-consuming process of developing analytical models can be accelerated with the help of machine learning, which is a technique for data processing. Using machine learning, antenna designers can quickly and intelligently optimize their physical antenna designs. This is achieved by developing trained models of the designers’ designs. Consequently, antenna designers can create more efficient antennas. Due to this, antenna designers can continue developing new designs despite the increasing complexity of antennas. When discussing this specific type of antenna, “Yagi” is frequently used as an abbreviation for “Yagi-Uda” the full name of the antenna. The length of the “driven dipole” may be equal to or shorter than the length of the “directors.” The ability to rapidly execute a diverse set of optimization algorithms and objectives, made possible by the trained models, makes it easier to conduct rapid comparisons and a diverse set of studies (including stochastic analysis for tolerance studies, etc.). While the device was operating at a frequency of ten gigahertz, the concept of two parasitic directors was conceived, and these parasitic directors were designed and optimized to improve the device’s directivity further. This antenna offers numerous advantages, one of which is its simplicity of manufacture. This antenna can be manufactured relatively easily due to its diminutive size and uncomplicated overall layout. The stochastic global search and optimization method known as simulated annealing (SA) is extraordinarily effective. This method is implemented instead of more conventional ones to achieve optimal element spacing.