Abstract
In this paper, a double layer stopband Frequency Selective Surface (FSS) for Ultra-Wide Band (UWB) applications is presented. The proposed FSS consists of two cascaded conventional FSS, one with double square loops and other with square loops. The − 10dB frequency band from 3.00 GHz to 11.64 GHz is achieved, with a fractional bandwidth of 149 %. Effects of angular stability and polarization independence over the frequency response are investigated and conclusions are presented. In order to show transmission performance, the FSS was simulated by ANSYS HFSS. The FSS shows angular stability and polarization independence in entire UWB band (3.10 – 10.60 GHz). A prototype was built and measurements were performed. A good agreement between simulated and measured results was observed.
Highlights
Frequency Selective Surfaces (FSS) are bi-dimensional periodic arrays of metallic patches on dielectric layer
In the neural network (NN) training, it was noticed that the learning rate of 0.006 and the mean square error to be reached equal to 0.05 in training stage were sufficient for the network to perform the FSS optimization at the desired frequency and bandwidth in the project. 80% of the data were used for training and the rest was used for validation
The radial base function (RBF)-NN training with the use of the FSS database generated by the double square loop geometry was performed with the following parameters: 4 neurons were inserted in the input layer corresponding to the resonance frequencies and bandwidths, 40 neurons in the hidden layer and 6 neurons in the output layer that correspond to the constructive parameters of the square loop geometry (h, p, d1, d2, w1, w2)
Summary
Frequency Selective Surfaces (FSS) are bi-dimensional periodic arrays of metallic patches on dielectric layer. FSS acts as a spatial filter which reflect some frequencies and transmit other frequencies This response is dependent on polarization and angle of incidence of electromagnetic waves, geometry of patch, periodicity of unit cells, thickness and permittivity of the substrate [1]. To optimize the design of FSS, neural networks were used
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