Frequency discriminators based on Sierpinski curve fractal FSS for 4-bit IFM systems using balanced binary code

Abstract

An Instantaneous Frequency Measurement (IFM) system consists of a set of elements capable of processing an input signal using discriminators to identify the sub-band to which the signal's frequency belongs. The displayed results are represented by binary values that reflect the output power of the discriminators. Therefore, it is of utmost importance to appropriately select the frequency response of these elements to ensure the proper functioning of the system. By designing the discriminators correctly, direct scanning can be achieved, eliminating the need for post-processing. In this thesis, evidence is presented that choosing a balanced binary code (BBC) to represent the output bits brings significant advantages in the design of frequency discriminators. The designed discriminators are composed of a set of four dual-band Frequency Selective Surfaces (FSS) based on the fractal geometry Sierpinski Curve, with different iterations, thus replacing traditional frequency discriminators such as interferometers and filters. As an application of the designed discriminators, a 4-bit IFM system will be hypothesized with a design resolution of 323.12 MHz for each of the 16 sub-bands distributed in the frequency range between 0.39 GHz and 5.56 GHz. The results of the computational simulations carried out in the CST Microwave Studio, along with the measurements using a network analyzer, were presented and compared, showing good correspondence. Both in measurements and in simulations, the FSS is placed between the antennas. The parameter analyzed in the simulations and measurements is the transmission coefficient, S21, between two horn-type antennas used in the measurements and represented by ports 1 and 2 in the computational simulations.