A Novel Design of a Robust Ten-Port Microwave Reflectometer With Autonomous Calibration by Using Neural Networks

In this study, a novel ten-port waveguide microwave sensor is designed, implemented, calibrated and tested in order to obtain the reflection coefficient magnitude and phase. This reflectometer is based on the well known six-port structure but the number of detectors has been increased to eight in order to improve the sampling procedure of the standing wave present within the waveguide. In addition, a learning method based on neural networks' usage has been implemented for autonomous calibration from the data collected by a vector network analyzer. An automated procedure consisting of a moving sample within a multimode cavity has enabled different reflection coefficients to be obtained. Neural networks have been employed in order to learn the relationship between the actual reflection parameter and the acquired signals from eight power detectors. This novel device has been calibrated with a neural architecture based on radial basis functions and the error of device measurements has been analyzed. This new design and the incorporated neural network calibration allow one to avoid problems caused by fault or nonlinearity of the detectors, and to get robustness, flexibility and adaptability characteristics for the presented device.