Arrangement and development of working characteristics of monopole antennas by various methods

In this thesis process, 2 different studies have been done. In one of the studies, by applying the "choke method", the circumference of the circular monopole antenna with an operating frequency of f = 1.5-2.5 GHz was covered with a copper choking ring. It has been observed that the designed choke ring significantly improves the recycling loss (S₁₁) of the monopole antenna. While designing the choke ring and monopole antenna, the physical properties of the ring (thickness, length, distance…) have been taken into consideration and the operating frequency and monopole antenna dimensions have been optimized. It showed a linear improvement in return loss values in the desired frequency band and made it possible to realize the design. Performance analyzes (loss of recycling, radiation pattern, etc.) were made and improvement was observed by placing the choking ring on the produced antenna at certain distances to the lower stone. The structure designed in a finite integration technique (FIT) the based simulation program was produced in the LPKF-E33 printed circuit device. Agilent Technologies N5234A PNA-L Network Analyzer was used to determine return loss values and radiation patterns. In another study, a two-layer wing design was applied on the lower stone of a circular monopole antenna with an operating frequency of f = 2 GHz, and as a result, it was aimed to create a multi-band dish structure by increasing the bandwidth of the antenna. Thus, a single antenna will be able to operate in various frequency bands by changing the wing angles without making any changes on it. In order to change the angles without contact, an artificial neural network (ANN) was developed and it was aimed that the network could detect some desired parameters, learn and make inferences as a result. While designing the network, the XOR problem was used in programming design. During the analyzes, the first and second wings were operated by changing their positions at the desired angles and various parametric values were obtained. The return loss obtained by changing the wing angles, the angle between the wings, the angle between the base and the wing, the bowl length, and the exit values were taught to the artificial neural network. Both designs have a small and easily integrated structure, with additional high precision and low cost.